Data preparation
#Read in the data from the updated database
con <- dbConnect(SQLite(),'Mr_EvidenceDB_30_03_2023')
#list tables
dbListTables(con) #list down the tables
## [1] "effectsizetype" "exposure" "methods" "outcome"
## [5] "results" "study" "studynotes"
results <- tbl(con, 'results')
colnames(results)
## [1] "results_id" "pmid" "methodid"
## [4] "effectsize" "lowerinterval" "upperinterval"
## [7] "pvalue" "se" "exposureid"
## [10] "outcomeid" "effectsizetype_id" "strata"
results <- data.frame(results)
#rename the columns
colnames(results) <- c("results_id","pmid","methodid","effectsize","lowerinterval",
"upperinterval", "pvalue","exposureid", "outcomeid",
"effectsizetype_id","se","strata" )
#results
#exposure
exposure <- tbl(con, 'exposure')
exposure <- data.frame(exposure)
colnames(exposure) #<- c("exposurename","exposureid")
## [1] "Exposureid_resultsid" "exposureid" "exposurename"
## [4] "exposuremeasured" "resultsid" "exposurenotes"
#exposure
#outcome
outcome <- tbl(con, 'outcome')
outcome <- data.frame(outcome)
colnames(outcome) #<- c("outcomename","outcomeid") #rename the columns
## [1] "outcomeid_resultsid" "outcomeid"
## [3] "outcomename" "outcomemeasured"
## [5] "resultsid" "totalsamplesize_outcome"
## [7] "X.cases_outcome" "control_outcome"
## [9] "outcomenotes"
#outcome
#methods
methods <- tbl(con, 'methods')
methods <- data.frame(methods)
colnames(methods) <- c("methodname","methodid") #rename the columns
#methods
#study
study <- tbl(con,'study')
study <- data.frame(study)
#study
#study notes
study_notes <- tbl(con,'studynotes')
study_notes <- data.frame(study_notes)
#study_notes
colnames(study_notes) <- c("notesid","pmid", "no_ofIVs","analysistype", "resultsid","unitsofmeasurement","notes")
#effectsizetype
effectsizetype <- tbl(con,'effectsizetype')
effectsizetype <- data.frame(effectsizetype)
#effectsizetype
#merge results table with effectsizetype tables
results_effectsizetype <- merge(results,effectsizetype,by.x = "effectsizetype_id", by.y = "id")
results_effectsizetype_methods <- merge(results_effectsizetype,methods, by.x="methodid",by.y = "methodid")
#results_effectsizetype_methods
#merging two dataframes results_effectsizetype_methods and exposure
results_effectsizetype_methods_exp <- merge(results_effectsizetype_methods, exposure,by.x = "results_id",by.y = "resultsid")
#merge the above dataframe and outcome
results_effectsizetype_methods_exp_out <- merge(results_effectsizetype_methods_exp,outcome, by.x = "results_id",by.y = "resultsid")
#merge with study notes
results_effectsizetype_methods_exp_out_studynotes <- merge(results_effectsizetype_methods_exp_out,study_notes,by.x = "results_id",by.y="resultsid")
#merge with study table
results_effectsizetype_methods_exp_out_studynotes_study <- merge(results_effectsizetype_methods_exp_out_studynotes,study, by.x = "pmid.y",by.y = "pmid")
mydata <- results_effectsizetype_methods_exp_out_studynotes_study
#make a unique column to takeup the y-axis
mydata$UID <- paste(mydata$author,mydata$pmid.x,mydata$year,mydata$results_id, sep = "_")
#Make a unique column with additonal study characteristics for annotating the forest plot
mydata$annotation <- paste(mydata$outcomemeasured,mydata$exposurenotes, mydata$outcomenotes,mydata$exposuremeasured,mydata$analysistype,
mydata$unitsofmeasurement,mydata$notes, sep = ",")
#write.table(x=mydata,"./mydata_excel.csv",sep = ",",col.names = TRUE,row.names = FALSE)
#slice_sample(mydata) #see a saple of random rows in random order
skim(mydata) #summarize the dataframe
Data summary
| Name |
mydata |
| Number of rows |
147 |
| Number of columns |
46 |
| _______________________ |
|
| Column type frequency: |
|
| character |
31 |
| numeric |
15 |
| ________________________ |
|
| Group variables |
None |
Variable type: character
| results_id |
0 |
1 |
2 |
4 |
0 |
147 |
0 |
| methodid |
0 |
1 |
2 |
3 |
0 |
10 |
0 |
| effectsizetype_id |
0 |
1 |
3 |
3 |
0 |
4 |
0 |
| exposureid.x |
0 |
1 |
2 |
2 |
0 |
3 |
0 |
| outcomeid.x |
0 |
1 |
2 |
2 |
0 |
5 |
0 |
| se |
0 |
1 |
3 |
5 |
0 |
23 |
0 |
| strata |
0 |
1 |
0 |
57 |
126 |
20 |
0 |
| effectsizetype |
0 |
1 |
2 |
14 |
0 |
4 |
0 |
| methodname |
0 |
1 |
3 |
21 |
0 |
10 |
0 |
| Exposureid_resultsid |
0 |
1 |
5 |
7 |
0 |
147 |
0 |
| exposureid.y |
0 |
1 |
2 |
2 |
0 |
3 |
0 |
| exposurename |
0 |
1 |
3 |
4 |
0 |
3 |
0 |
| exposuremeasured |
0 |
1 |
0 |
189 |
3 |
38 |
0 |
| exposurenotes |
0 |
1 |
5 |
9 |
0 |
2 |
0 |
| outcomeid_resultsid |
0 |
1 |
5 |
7 |
0 |
147 |
0 |
| outcomeid.y |
0 |
1 |
2 |
2 |
0 |
5 |
0 |
| outcomename |
0 |
1 |
3 |
26 |
0 |
5 |
0 |
| outcomemeasured |
0 |
1 |
0 |
185 |
86 |
46 |
0 |
| outcomenotes |
0 |
1 |
0 |
43 |
1 |
21 |
0 |
| notesid |
0 |
1 |
2 |
4 |
0 |
147 |
0 |
| no_ofIVs |
0 |
1 |
1 |
3 |
0 |
34 |
0 |
| analysistype |
0 |
1 |
4 |
11 |
0 |
3 |
0 |
| unitsofmeasurement |
0 |
1 |
0 |
102 |
16 |
85 |
0 |
| notes |
0 |
1 |
0 |
212 |
8 |
130 |
0 |
| title |
0 |
1 |
80 |
165 |
0 |
28 |
0 |
| studyaim |
0 |
1 |
54 |
160 |
0 |
28 |
0 |
| population |
0 |
1 |
3 |
10 |
0 |
3 |
0 |
| sex |
0 |
1 |
4 |
6 |
0 |
2 |
0 |
| author |
0 |
1 |
10 |
31 |
0 |
25 |
0 |
| UID |
0 |
1 |
29 |
49 |
0 |
147 |
0 |
| annotation |
0 |
1 |
66 |
536 |
0 |
144 |
0 |
Variable type: numeric
| pmid.y |
0 |
1 |
32135067.81 |
3408389.13 |
19470880.00 |
31348509.50 |
33323262.00 |
34120448.00 |
35947639.00 |
▁▁▁▂▇ |
| pmid.x |
0 |
1 |
32135067.81 |
3408389.13 |
19470880.00 |
31348509.50 |
33323262.00 |
34120448.00 |
35947639.00 |
▁▁▁▂▇ |
| effectsize |
0 |
1 |
1.37 |
2.30 |
-1.11 |
0.24 |
1.08 |
1.38 |
15.53 |
▇▁▁▁▁ |
| lowerinterval |
0 |
1 |
0.69 |
1.39 |
-1.80 |
0.00 |
0.21 |
1.14 |
13.91 |
▇▁▁▁▁ |
| upperinterval |
0 |
1 |
1.31 |
2.07 |
0.00 |
0.27 |
1.08 |
1.56 |
17.14 |
▇▁▁▁▁ |
| pvalue |
0 |
1 |
0.07 |
0.18 |
0.00 |
0.00 |
0.00 |
0.02 |
0.93 |
▇▁▁▁▁ |
| totalsamplesize_outcome |
0 |
1 |
105092.34 |
156741.00 |
0.00 |
1383.50 |
29247.00 |
135585.00 |
553225.00 |
▇▁▁▁▁ |
| X.cases_outcome |
0 |
1 |
17027.68 |
34905.62 |
0.00 |
0.00 |
0.00 |
10125.00 |
199731.00 |
▇▂▁▁▁ |
| control_outcome |
0 |
1 |
65951.59 |
129427.58 |
0.00 |
0.00 |
0.00 |
74345.00 |
482997.00 |
▇▁▁▁▁ |
| mean_age |
0 |
1 |
3.31 |
13.19 |
0.00 |
0.00 |
0.00 |
0.00 |
60.00 |
▇▁▁▁▁ |
| median_age |
0 |
1 |
1.16 |
8.07 |
0.00 |
0.00 |
0.00 |
0.00 |
56.87 |
▇▁▁▁▁ |
| lower_age |
0 |
1 |
2.03 |
8.28 |
0.00 |
0.00 |
0.00 |
0.00 |
40.00 |
▇▁▁▁▁ |
| upper_age |
0 |
1 |
4.78 |
19.02 |
0.00 |
0.00 |
0.00 |
0.00 |
100.00 |
▇▁▁▁▁ |
| year |
0 |
1 |
2019.53 |
2.61 |
2009.00 |
2019.00 |
2021.00 |
2021.00 |
2022.00 |
▁▁▁▂▇ |
| samplesize |
0 |
1 |
184760.80 |
190867.39 |
0.00 |
38662.00 |
67553.00 |
337536.00 |
553225.00 |
▇▁▂▂▂ |
summary(mydata)
## pmid.y results_id methodid effectsizetype_id
## Min. :19470880 Length:147 Length:147 Length:147
## 1st Qu.:31348510 Class :character Class :character Class :character
## Median :33323262 Mode :character Mode :character Mode :character
## Mean :32135068
## 3rd Qu.:34120448
## Max. :35947639
## pmid.x effectsize lowerinterval upperinterval
## Min. :19470880 Min. :-1.1090 Min. :-1.8000 Min. : 0.0000
## 1st Qu.:31348510 1st Qu.: 0.2445 1st Qu.: 0.0000 1st Qu.: 0.2745
## Median :33323262 Median : 1.0800 Median : 0.2100 Median : 1.0850
## Mean :32135068 Mean : 1.3742 Mean : 0.6867 Mean : 1.3089
## 3rd Qu.:34120448 3rd Qu.: 1.3850 3rd Qu.: 1.1400 3rd Qu.: 1.5550
## Max. :35947639 Max. :15.5270 Max. :13.9090 Max. :17.1440
## pvalue exposureid.x outcomeid.x se
## Min. :0.00000 Length:147 Length:147 Length:147
## 1st Qu.:0.00000 Class :character Class :character Class :character
## Median :0.00001 Mode :character Mode :character Mode :character
## Mean :0.06977
## 3rd Qu.:0.01660
## Max. :0.93000
## strata effectsizetype methodname Exposureid_resultsid
## Length:147 Length:147 Length:147 Length:147
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## exposureid.y exposurename exposuremeasured exposurenotes
## Length:147 Length:147 Length:147 Length:147
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## outcomeid_resultsid outcomeid.y outcomename outcomemeasured
## Length:147 Length:147 Length:147 Length:147
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## totalsamplesize_outcome X.cases_outcome control_outcome outcomenotes
## Min. : 0 Min. : 0 Min. : 0 Length:147
## 1st Qu.: 1384 1st Qu.: 0 1st Qu.: 0 Class :character
## Median : 29247 Median : 0 Median : 0 Mode :character
## Mean :105092 Mean : 17028 Mean : 65952
## 3rd Qu.:135585 3rd Qu.: 10125 3rd Qu.: 74345
## Max. :553225 Max. :199731 Max. :482997
## notesid no_ofIVs analysistype unitsofmeasurement
## Length:147 Length:147 Length:147 Length:147
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## notes title studyaim population
## Length:147 Length:147 Length:147 Length:147
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## sex mean_age median_age lower_age
## Length:147 Min. : 0.000 Min. : 0.000 Min. : 0.000
## Class :character 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Mode :character Median : 0.000 Median : 0.000 Median : 0.000
## Mean : 3.314 Mean : 1.161 Mean : 2.034
## 3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.: 0.000
## Max. :60.000 Max. :56.870 Max. :40.000
## upper_age year samplesize author
## Min. : 0.000 Min. :2009 Min. : 0 Length:147
## 1st Qu.: 0.000 1st Qu.:2019 1st Qu.: 38662 Class :character
## Median : 0.000 Median :2021 Median : 67553 Mode :character
## Mean : 4.782 Mean :2020 Mean :184761
## 3rd Qu.: 0.000 3rd Qu.:2021 3rd Qu.:337536
## Max. :100.000 Max. :2022 Max. :553225
## UID annotation
## Length:147 Length:147
## Class :character Class :character
## Mode :character Mode :character
##
##
##
library(metafor)
library(tidyverse)
dat1 <- read.csv("mydata_excel_updated.csv",header = TRUE) #read in the data
#make a unique id without results id
#make a unique column to takeup the y-axis
dat1$ID <- paste(dat1$author,dat1$pmid.y,dat1$year, sep = "_")
dat1
## pmid.y results_id AnalysisType OUTCOMEID EXPOSURE NO_ofIVs methodid
## 1 19470880 R57 Main O2 BMI 2 M10
## 2 19470880 R58 sensitivity O3 BMI 2 M10
## 3 23824655 R3 Main O3 BMI 1 M5
## 4 23824655 R4 Main O2 BMI 1 M5
## 5 23824655 R2 sensitivity O1 BMI 1 M5
## 6 23824655 R1 Main O1 BMI 1 M5
## 7 24462370 R92 Main O2 BMI 14 M5
## 8 24462370 R93 Main O3 BMI 14 M5
## 9 25712996 R77 Main O3 BMI 32 M5
## 10 25712996 R79 sensitivity O3 BMI 32 M5
## 11 25712996 R80 sensitivity O3 BMI 32 M5
## 12 25712996 R81 sensitivity O2 BMI 32 M5
## 13 25712996 R82 sensitivity O2 BMI 32 M5
## 14 25712996 R83 sensitivity O3 BMI 32 M5
## 15 25712996 R84 sensitivity O2 BMI 32 M5
## 16 25712996 R78 Main O2 BMI 32 M5
## 17 25712996 R86 sensitivity O2 BMI 32 M5
## 18 25712996 R85 sensitivity O3 BMI 32 M5
## 19 26568383 R7 sensitivity O2 BMI 1 M10
## 20 26568383 R5 Main O2 BMI 32 M10
## 21 26568383 R6 sensitivity O2 BMI 31 M10
## 22 28678979 R44 sensitivity O3 BMI 93 M10
## 23 28678979 R43 sensitivity O2 BMI 93 M10
## 24 28678979 R42 Main O1 BMI 93 M10
## 25 30045251 R108 Main O1 BMI 2 M5
## 26 30045251 R109 Main O1 BMI 2 M5
## 27 30462199 R74 sensitivity O2 BMI 96 M4
## 28 30462199 R76 sensitivity O2 BMI 96 M2
## 29 30462199 R73 Main O2 BMI 96 M1
## 30 30462199 R75 sensitivity O2 BMI 96 M8
## 31 30707692 R59 Main O1 BMI 97 M1
## 32 31195408 R64 sensitivity O1 BMI 96 M2
## 33 31195408 R65 sensitivity O1 BMI 96 M4
## 34 31195408 R67 sensitivity O1 Fat mass index 82 M6
## 35 31195408 R63 Main O1 BMI 96 M1
## 36 31195408 R68 sensitivity O1 Fat mass index 82 M6
## 37 31195408 R66 sensitivity O1 BMI 96 M11
## 38 31501611 R10 sensitivity O1 VAT 44 M10
## 39 31501611 R8 Main O1 VAT 44 M1
## 40 31501611 R11 sensitivity O1 VAT 44 M10
## 41 31501611 R9 Main O1 VAT 44 M1
## 42 31708716 R41 Main O3 BMI 96 M10
## 43 32636122 R123 Main O3 WHR 5 M10
## 44 32636122 R121 Main O2 WHR 5 M10
## 45 32636122 R122 Main O3 BMI 5 M10
## 46 32636122 R120 Main O2 BMI 5 M10
## 47 32665587 R15 sensitivity O1 BMI 79 M10
## 48 32665587 R14 Main O1 BMI 79 M10
## 49 32665587 R19 sensitivity O1 BMI 79 M3
## 50 32665587 R20 sensitivity O1 BMI 79 M4
## 51 32665587 R21 sensitivity O1 BMI 64 M1
## 52 32665587 R22 sensitivity O1 BMI 64 M2
## 53 32665587 R17 sensitivity O1 BMI 79 M1
## 54 32665587 R16 sensitivity O1 BMI 79 M1
## 55 32665587 R23 sensitivity O1 BMI 64 M3
## 56 32665587 R18 sensitivity O1 BMI 79 M2
## 57 32665587 R13 Main O1 BMI 79 M10
## 58 32665587 R24 sensitivity O1 BMI 64 M4
## 59 32712226 R124 Main O4 BMI 64 M1
## 60 33131310 R47 Main O1 BMI 812 M1
## 61 33131310 R51 sensitivity O1 BMI 810 M11
## 62 33131310 R54 sensitivity O1 BMI 832 M4
## 63 33131310 R55 sensitivity O1 BMI 816 M11
## 64 33131310 R52 sensitivity O1 BMI 832 M1
## 65 33131310 R49 sensitivity O1 BMI 812 M2
## 66 33131310 R50 sensitivity O1 BMI 812 M4
## 67 33131310 R48 sensitivity O1 BMI 812 M1
## 68 33131310 R56 sensitivity O1 BMI 832 M1
## 69 33131310 R53 sensitivity O1 BMI 832 M2
## 70 33323262 R88 sensitivity O1 BMI 76 M2
## 71 33323262 R91 sensitivity O1 BMI 59 M11
## 72 33323262 R89 sensitivity O1 BMI 76 M3
## 73 33323262 R90 sensitivity O1 BMI 76 M4
## 74 33323262 R87 Main O1 BMI 76 M1
## 75 33771188 R27 sensitivity O1 BMI 13 M3
## 76 33771188 R29 sensitivity O1 BMI 13 M1
## 77 33771188 R30 sensitivity O1 BMI 13 M2
## 78 33771188 R35 sensitivity O1 BMI 76 M3
## 79 33771188 R32 sensitivity O1 BMI 13 M4
## 80 33771188 R33 sensitivity O1 BMI 76 M1
## 81 33771188 R34 sensitivity O1 BMI 76 M2
## 82 33771188 R39 sensitivity O1 BMI 76 M3
## 83 33771188 R28 sensitivity O1 BMI 13 M4
## 84 33771188 R40 sensitivity O1 BMI 76 M4
## 85 33771188 R38 sensitivity O1 BMI 76 M2
## 86 33771188 R25 sensitivity O1 BMI 13 M1
## 87 33771188 R31 sensitivity O1 BMI 13 M3
## 88 33771188 R36 sensitivity O1 BMI 76 M4
## 89 33771188 R37 sensitivity O1 BMI 76 M1
## 90 33771188 R26 sensitivity O1 BMI 13 M2
## 91 33980691 R101 Main O1 body fat% 38 M2
## 92 33980691 R99 sensitivity O1 bodyfat%-FA 36 M1
## 93 33980691 R96 Main O1 body fat% 38 M1
## 94 33980691 R106 sensitivity O1 body fat% 36 M4
## 95 33980691 R107 sensitivity O1 bodyfat%-FA 36 M2
## 96 33980691 R103 sensitivity O1 body fat% 36 M2
## 97 33980691 R100 sensitivity O1 body fat% 38 M4
## 98 33980691 R98 Main O1 body fat% 38 M1
## 99 33980691 R102 sensitivity O1 body fat% 36 M4
## 100 33980691 R105 sensitivity O1 body fat% 38 M2
## 101 33980691 R97 Main O1 body fat% 36 M1
## 102 33980691 R104 sensitivity O1 body fat% 38 M4
## 103 34001814 R112 Main O1 BMI 16 M1
## 104 34001814 R111 sensitivity O1 BMI 7 M2
## 105 34001814 R110 Main O1 BMI 7 M1
## 106 34001814 R113 sensitivity O1 BMI 16 M2
## 107 34120448 R130 sensitivity O2 WHR 324 M10
## 108 34120448 R128 sensitivity O2 VAT 208 M10
## 109 34120448 R132 sensitivity O2 VAT 208 M10
## 110 34120448 R125 Main O2 BMI 565 M10
## 111 34120448 R138 sensitivity O3 WHR 324 M10
## 112 34120448 R131 sensitivity O2 body fat% 81 M10
## 113 34120448 R127 sensitivity O2 body fat% 81 M10
## 114 34120448 R129 Main O2 BMI 565 M10
## 115 34120448 R141 sensitivity O2 BMI 565 M10
## 116 34120448 R142 sensitivity O2 WHR 324 M10
## 117 34120448 R143 sensitivity O2 body fat% 81 M10
## 118 34120448 R144 sensitivity O2 BMI 208 M10
## 119 34120448 R133 Main O3 BMI 565 M10
## 120 34120448 R134 sensitivity O3 WHR 324 M10
## 121 34120448 R135 sensitivity O3 body fat% 81 M10
## 122 34120448 R136 sensitivity O3 VAT 208 M10
## 123 34120448 R148 sensitivity O3 VAT 208 M10
## 124 34120448 R137 Main O3 BMI 565 M10
## 125 34120448 R139 sensitivity O3 body fat% 81 M10
## 126 34120448 R147 sensitivity O3 body fat% 81 M10
## 127 34120448 R140 Main O3 VAT 208 M10
## 128 34120448 R146 sensitivity O3 WHR 324 M10
## 129 34120448 R145 sensitivity O3 BMI 565 M10
## 130 34465205 R95 Main O1 BMI 295 M1
## 131 35074047 R71 sensitivity O1 bodyfat%-FA 36 M1
## 132 35074047 R69 Main O1 BMI 73 M1
## 133 35074047 R72 sensitivity O1 bodyfat%-UFA 38 M1
## 134 35074047 R70 sensitivity O1 bodyfat% 696 M1
## 135 35232963 R12 Main O3 BMI 93 M10
## 136 35599089 R116 sensitivity O2 fat mass% 4 M10
## 137 35599089 R115 sensitivity O2 WHtR 4 M10
## 138 35599089 R114 Main O2 BMI 4 M10
## 139 35599089 R119 sensitivity O3 fat mass% 4 M10
## 140 35599089 R118 sensitivity O3 WHtR 4 M10
## 141 35599089 R117 Main O3 BMI 4 M10
## 142 35656995 R94 Main O6 BMI 0 M1
## 143 35694671 R62 sensitivity O1 BMI 14 M4
## 144 35694671 R61 sensitivity O1 BMI 14 M2
## 145 35694671 R60 Main O1 BMI 14 M1
## 146 35947639 R45 Main O2 BMI 95 M10
## 147 35947639 R46 sensitivity O2 BMI 95 M9
## GWASofexposure effectsizetype_id
## 1 ID4
## 2 ID4
## 3 Metaanalysis of17 GWAs(SD = 4.62kg/m2) ID4
## 4 Metaanalysis of17 GWAs(SD = 4.62kg/m2) ID4
## 5 Metaanalysis of17 GWAs(SD = 4.62kg/m2) ID1
## 6 Metaanalysis of17 GWAs(SD = 4.62kg/m2) ID1
## 7 ID4
## 8 ID4
## 9 ID4
## 10 ID4
## 11 ID4
## 12 ID4
## 13 ID4
## 14 ID4
## 15 ID4
## 16 ID4
## 17 ID4
## 18 ID4
## 19 ID2
## 20 ID2
## 21 ID2
## 22 ID4
## 23 ID4
## 24 ID1
## 25 ID1
## 26 ID1
## 27 ID4
## 28 ID4
## 29 ID4
## 30 ID4
## 31 ID1
## 32 ID1
## 33 ID1
## 34 ID1
## 35 ID1
## 36 ID1
## 37 ID1
## 38 ID1
## 39 ID1
## 40 ID1
## 41 ID1
## 42 ID4
## 43 ID4
## 44 ID4
## 45 ID4
## 46 ID4
## 47 ID5
## 48 ID5
## 49 ID5
## 50 ID5
## 51 ID5
## 52 ID5
## 53 ID5
## 54 ID5
## 55 ID5
## 56 ID5
## 57 ID5
## 58 ID5
## 59 ID4
## 60 ID1
## 61 ID1
## 62 ID1
## 63 ID1
## 64 ID1
## 65 ID1
## 66 ID1
## 67 ID1
## 68 ID1
## 69 ID1
## 70 ID1
## 71 ID1
## 72 ID1
## 73 ID1
## 74 ID1
## 75 ID1
## 76 ID1
## 77 ID1
## 78 ID1
## 79 ID1
## 80 ID1
## 81 ID1
## 82 ID1
## 83 ID1
## 84 ID1
## 85 ID1
## 86 ID1
## 87 ID1
## 88 ID1
## 89 ID1
## 90 ID1
## 91 ID4
## 92 ID4
## 93 ID1
## 94 ID4
## 95 ID4
## 96 ID4
## 97 ID4
## 98 ID4
## 99 ID4
## 100 ID4
## 101 ID1
## 102 ID4
## 103 ID1
## 104 ID1
## 105 ID1
## 106 ID1
## 107 ID4
## 108 ID4
## 109 ID4
## 110 ID4
## 111 ID4
## 112 ID4
## 113 ID4
## 114 ID4
## 115 ID4
## 116 ID4
## 117 ID4
## 118 ID4
## 119 ID4
## 120 ID4
## 121 ID4
## 122 ID4
## 123 ID4
## 124 ID4
## 125 ID4
## 126 ID4
## 127 ID4
## 128 ID4
## 129 ID4
## 130 ID1
## 131 UKBiobank(Martin et al 2021) ID1
## 132 Locke et al;2015 ID1
## 133 UKBiobank(Martin et al 2021) ID1
## 134 UKBiobank(Martin et al 2021) ID1
## 135 ID5
## 136 ID4
## 137 ID4
## 138 ID4
## 139 ID4
## 140 ID4
## 141 ID4
## 142 ID1
## 143 ID1
## 144 ID1
## 145 ID1
## 146 ID4
## 147 ID4
## UID effectsize
## 1 Nicholas J Timpson et al_19470880_2009_R57 0.385
## 2 Nicholas J Timpson et al_19470880_2009_R58 0.179
## 3 Tove Fall et al_23824655_2013_R3 0.490
## 4 Tove Fall et al_23824655_2013_R4 0.892
## 5 Tove Fall et al_23824655_2013_R2 1.093
## 6 Tove Fall et al_23824655_2013_R1 1.128
## 7 Michael V Holmes_24462370_2014_R92 0.700
## 8 Michael V Holmes_24462370_2014_R93 0.280
## 9 Tove Fall et al_25712996_2015_R77 0.150
## 10 Tove Fall et al_25712996_2015_R79 0.210
## 11 Tove Fall et al_25712996_2015_R80 0.001
## 12 Tove Fall et al_25712996_2015_R81 0.210
## 13 Tove Fall et al_25712996_2015_R82 0.060
## 14 Tove Fall et al_25712996_2015_R83 0.070
## 15 Tove Fall et al_25712996_2015_R84 0.100
## 16 Tove Fall et al_25712996_2015_R78 0.160
## 17 Tove Fall et al_25712996_2015_R86 0.210
## 18 Tove Fall et al_25712996_2015_R85 0.230
## 19 LouiseAC Millard et al_26568383_2015_R7 0.385
## 20 LouiseAC Millard et al_26568383_2015_R5 0.305
## 21 LouiseAC Millard et al_26568383_2015_R6 0.290
## 22 Donald M loyal et al_28678979_2017_R44 1.370
## 23 Donald M loyal et al_28678979_2017_R43 1.650
## 24 Donald M loyal et al_28678979_2017_R42 1.640
## 25 Mee-Ri Lee_30045251_2018_R108 1.250
## 26 Mee-Ri Lee_30045251_2018_R109 1.260
## 27 Wes Spiller et al_30462199_2018_R74 0.027
## 28 Wes Spiller et al_30462199_2018_R76 0.147
## 29 Wes Spiller et al_30462199_2018_R73 0.101
## 30 Wes Spiller et al_30462199_2018_R75 -0.020
## 31 Louise A.C.Millard_30707692_2019_R59 1.077
## 32 Susanna C. Larsson et al_31195408_2020_R64 1.110
## 33 Susanna C. Larsson et al_31195408_2020_R65 1.060
## 34 Susanna C. Larsson et al_31195408_2020_R67 1.120
## 35 Susanna C. Larsson et al_31195408_2020_R63 1.100
## 36 Susanna C. Larsson et al_31195408_2020_R68 1.080
## 37 Susanna C. Larsson et al_31195408_2020_R66 1.100
## 38 Torgny Karlsson et al_31501611_2019_R10 3.510
## 39 Torgny Karlsson et al_31501611_2019_R8 2.610
## 40 Torgny Karlsson et al_31501611_2019_R11 2.000
## 41 Torgny Karlsson et al_31501611_2019_R9 1.860
## 42 Frank Windmeijer et al_31708716_2018_R41 0.087
## 43 Qiying Song_32636122_2020_R123 3.209
## 44 Qiying Song_32636122_2020_R121 7.277
## 45 Qiying Song_32636122_2020_R122 7.471
## 46 Qiying Song_32636122_2020_R120 15.527
## 47 Ben Brompton et al_32665587_2020_R15 1.130
## 48 Ben Brompton et al_32665587_2020_R14 1.840
## 49 Ben Brompton et al_32665587_2020_R19 1.420
## 50 Ben Brompton et al_32665587_2020_R20 0.470
## 51 Ben Brompton et al_32665587_2020_R21 1.260
## 52 Ben Brompton et al_32665587_2020_R22 1.380
## 53 Ben Brompton et al_32665587_2020_R17 0.760
## 54 Ben Brompton et al_32665587_2020_R16 0.830
## 55 Ben Brompton et al_32665587_2020_R23 1.380
## 56 Ben Brompton et al_32665587_2020_R18 0.890
## 57 Ben Brompton et al_32665587_2020_R13 1.590
## 58 Ben Brompton et al_32665587_2020_R24 0.920
## 59 Timothy E. Thayer_32712226_2021_R124 1.100
## 60 Van Oort Sabine et al_33131310_2020_R47 1.420
## 61 Van Oort Sabine et al_33131310_2020_R51 1.460
## 62 Van Oort Sabine et al_33131310_2020_R54 1.070
## 63 Van Oort Sabine et al_33131310_2020_R55 1.490
## 64 Van Oort Sabine et al_33131310_2020_R52 1.420
## 65 Van Oort Sabine et al_33131310_2020_R49 1.340
## 66 Van Oort Sabine et al_33131310_2020_R50 1.040
## 67 Van Oort Sabine et al_33131310_2020_R48 1.430
## 68 Van Oort Sabine et al_33131310_2020_R56 1.290
## 69 Van Oort Sabine et al_33131310_2020_R53 1.280
## 70 Elina Hypponen_33323262_2019_R88 1.400
## 71 Elina Hypponen_33323262_2019_R91 1.550
## 72 Elina Hypponen_33323262_2019_R89 1.430
## 73 Elina Hypponen_33323262_2019_R90 1.100
## 74 Elina Hypponen_33323262_2019_R87 1.550
## 75 Shan-Shan Dong et al_33771188_2021_R27 1.110
## 76 Shan-Shan Dong et al_33771188_2021_R29 1.140
## 77 Shan-Shan Dong et al_33771188_2021_R30 1.130
## 78 Shan-Shan Dong et al_33771188_2021_R35 1.230
## 79 Shan-Shan Dong et al_33771188_2021_R32 1.370
## 80 Shan-Shan Dong et al_33771188_2021_R33 1.210
## 81 Shan-Shan Dong et al_33771188_2021_R34 1.230
## 82 Shan-Shan Dong et al_33771188_2021_R39 1.300
## 83 Shan-Shan Dong et al_33771188_2021_R28 1.210
## 84 Shan-Shan Dong et al_33771188_2021_R40 1.250
## 85 Shan-Shan Dong et al_33771188_2021_R38 1.300
## 86 Shan-Shan Dong et al_33771188_2021_R25 1.120
## 87 Shan-Shan Dong et al_33771188_2021_R31 1.140
## 88 Shan-Shan Dong et al_33771188_2021_R36 1.140
## 89 Shan-Shan Dong et al_33771188_2021_R37 1.310
## 90 Shan-Shan Dong et al_33771188_2021_R26 1.110
## 91 Susan Martin et al_33980691_2021_R101 0.740
## 92 Susan Martin et al_33980691_2021_R99 -0.569
## 93 Susan Martin et al_33980691_2021_R96 3.030
## 94 Susan Martin et al_33980691_2021_R106 -0.283
## 95 Susan Martin et al_33980691_2021_R107 -1.109
## 96 Susan Martin et al_33980691_2021_R103 -0.507
## 97 Susan Martin et al_33980691_2021_R100 0.408
## 98 Susan Martin et al_33980691_2021_R98 0.690
## 99 Susan Martin et al_33980691_2021_R102 -0.098
## 100 Susan Martin et al_33980691_2021_R105 1.138
## 101 Susan Martin et al_33980691_2021_R97 0.340
## 102 Susan Martin et al_33980691_2021_R104 1.159
## 103 Jingwen Fan_34001814_2021_R112 1.005
## 104 Jingwen Fan_34001814_2021_R111 1.009
## 105 Jingwen Fan_34001814_2021_R110 1.008
## 106 Jingwen Fan_34001814_2021_R113 1.007
## 107 Alice Giontella_34120448_2021_R130 0.546
## 108 Alice Giontella_34120448_2021_R128 0.336
## 109 Alice Giontella_34120448_2021_R132 0.226
## 110 Alice Giontella_34120448_2021_R125 0.233
## 111 Alice Giontella_34120448_2021_R138 0.676
## 112 Alice Giontella_34120448_2021_R131 0.142
## 113 Alice Giontella_34120448_2021_R127 0.241
## 114 Alice Giontella_34120448_2021_R129 0.205
## 115 Alice Giontella_34120448_2021_R141 0.158
## 116 Alice Giontella_34120448_2021_R142 0.048
## 117 Alice Giontella_34120448_2021_R143 -0.161
## 118 Alice Giontella_34120448_2021_R144 0.122
## 119 Alice Giontella_34120448_2021_R133 0.257
## 120 Alice Giontella_34120448_2021_R134 0.367
## 121 Alice Giontella_34120448_2021_R135 0.269
## 122 Alice Giontella_34120448_2021_R136 0.296
## 123 Alice Giontella_34120448_2021_R148 0.190
## 124 Alice Giontella_34120448_2021_R137 0.248
## 125 Alice Giontella_34120448_2021_R139 0.267
## 126 Alice Giontella_34120448_2021_R147 -0.033
## 127 Alice Giontella_34120448_2021_R140 0.284
## 128 Alice Giontella_34120448_2021_R146 0.055
## 129 Alice Giontella_34120448_2021_R145 0.172
## 130 Grace M. Power_34465205_2021_R95 1.770
## 131 Susan Martin et al_35074047_2022_R71 0.340
## 132 Susan Martin et al_35074047_2022_R69 2.180
## 133 Susan Martin et al_35074047_2022_R72 3.030
## 134 Susan Martin et al_35074047_2022_R70 2.010
## 135 Carlos Cinelli et al_35232963_2022_R12 0.145
## 136 Liwan Fu et al_35599089_2022_R116 11.460
## 137 Liwan Fu et al_35599089_2022_R115 6.994
## 138 Liwan Fu et al_35599089_2022_R114 14.374
## 139 Liwan Fu et al_35599089_2022_R119 7.908
## 140 Liwan Fu et al_35599089_2022_R118 2.235
## 141 Liwan Fu et al_35599089_2022_R117 7.869
## 142 Nataraja Sarma Vaitinadin et al_35656995_2022_R94 1.890
## 143 Wenting Wang et al_35694671_2022_R62 1.950
## 144 Wenting Wang et al_35694671_2022_R61 1.490
## 145 Wenting Wang et al_35694671_2022_R60 1.390
## 146 Wes Spiller et al_35947639_2022_R45 0.130
## 147 Wes Spiller et al_35947639_2022_R46 0.034
## effectsize_per1SD lowerinterval LI_per1SD upperinterval UI_per1SD pvalue
## 1 0.9625 0.18800 0.47000 0.58300 1.45800 2.00e-04
## 2 0.4474 0.06800 0.17000 0.29000 0.72500 2.00e-03
## 3 0.4900 0.18700 0.18700 0.79300 0.79300 2.00e-03
## 4 0.8920 0.47500 0.47500 1.30900 1.30900 2.80e-05
## 5 1.4300 0.78300 0.78300 1.52700 3.43500 6.00e-01
## 6 1.5910 1.07000 1.32300 1.18900 1.87300 7.00e-06
## 7 0.7000 0.24000 0.24000 1.16000 1.16000 0.00e+00
## 8 0.2800 0.03000 0.03000 0.52000 0.52000 0.00e+00
## 9 0.1500 0.03000 0.03000 0.26000 0.26000 1.00e-02
## 10 0.2100 0.09000 0.09000 0.33000 0.33000 8.00e-04
## 11 0.0010 -0.16000 -0.16000 0.18000 0.18000 9.30e-01
## 12 0.2100 0.12000 0.12000 0.30000 0.30000 2.50e-06
## 13 0.0600 -0.14000 -0.14000 0.26000 0.26000 5.60e-01
## 14 0.0700 -0.04000 -0.04000 0.18000 0.18000 2.40e-01
## 15 0.1000 -0.01000 -0.01000 0.20000 0.20000 6.00e-02
## 16 0.1600 0.04000 0.04000 0.28000 0.28000 1.00e-02
## 17 0.2100 0.04000 0.04000 0.37000 0.37000 2.00e-02
## 18 0.2300 0.00600 0.00600 0.40000 0.40000 8.00e-03
## 19 0.3850 -0.00500 -0.00500 0.83000 0.83000 8.60e-02
## 20 0.3050 0.13000 0.13000 0.48000 0.48000 1.00e-03
## 21 0.2900 0.10000 0.10000 0.48000 0.48000 2.00e-03
## 22 1.3700 0.88000 0.88000 1.85000 1.85000 3.60e-08
## 23 1.6500 0.78000 0.78000 2.52000 2.52000 2.00e-04
## 24 1.6400 1.48000 1.48000 1.83000 1.83000 1.10e-19
## 25 1.2500 0.00000 0.00000 0.00000 0.00000 2.70e-02
## 26 1.2600 0.00000 0.00000 0.00000 0.00000 2.50e-02
## 27 0.0270 -0.09000 -0.09000 0.15000 0.15000 6.58e-01
## 28 0.1470 0.08000 0.08000 0.21000 0.21000 3.20e-02
## 29 0.1010 0.04000 0.04000 0.16000 0.16000 1.00e-03
## 30 -0.0200 -0.32000 -0.32000 0.28000 0.28000 3.25e-01
## 31 1.5740 1.06800 1.50700 1.08500 1.63300 0.00e+00
## 32 1.1100 1.09000 1.09000 1.13000 1.13000 3.90e-30
## 33 1.0600 1.01000 1.01000 1.12000 1.12000 2.00e-02
## 34 1.1200 1.04000 1.04000 1.20000 1.20000 1.50e-03
## 35 1.4770 1.07000 1.33400 1.12000 1.57200 3.40e-16
## 36 1.0800 0.99000 0.99000 1.19000 1.19000 9.00e-02
## 37 1.1000 1.08000 1.08000 1.12000 1.12000 0.00e+00
## 38 3.5100 2.71000 2.71000 4.54000 4.54000 2.50e-21
## 39 2.6100 2.14000 2.14000 3.19000 3.19000 3.10e-21
## 40 2.0000 1.75000 1.75000 2.28000 2.28000 4.50e-24
## 41 1.8600 1.65000 1.65000 2.10000 2.10000 1.40e-24
## 42 0.0870 0.05564 0.05564 0.11836 0.11836 0.00e+00
## 43 3.2090 -0.36500 -0.36500 6.05200 6.05200 5.30e-02
## 44 7.2770 0.60900 0.60900 13.94600 13.94600 3.20e-02
## 45 7.4710 6.35500 6.35500 8.58800 8.58800 2.73e-39
## 46 15.5270 13.90900 13.90900 17.14400 17.14400 5.66e-79
## 47 1.1300 0.04000 0.04000 2.21000 2.21000 4.19e-02
## 48 1.8400 1.20000 1.20000 2.47000 2.47000 1.44e-08
## 49 1.4200 -1.80000 -1.80000 4.64000 4.64000 3.87e-01
## 50 0.4700 -1.18000 -1.18000 2.11000 2.11000 5.79e-01
## 51 1.2600 0.90000 0.90000 1.63000 1.63000 5.59e-09
## 52 1.3800 0.87000 0.87000 1.89000 1.89000 9.67e-08
## 53 0.7600 -0.19000 -0.19000 1.70000 1.70000 1.17e-01
## 54 0.8300 -0.11000 -0.11000 1.77000 1.77000 8.25e-02
## 55 1.3800 0.79000 0.79000 1.98000 1.98000 2.16e-05
## 56 0.8900 -0.78000 -0.78000 2.57000 2.57000 2.96e-01
## 57 1.5900 1.34000 1.34000 1.83000 1.83000 1.26e-36
## 58 0.9200 0.22000 0.22000 1.62000 1.62000 1.26e-02
## 59 1.1000 0.00000 0.00000 0.00000 0.00000 4.00e-03
## 60 1.4200 1.37000 1.37000 1.48000 1.48000 3.12e-81
## 61 1.4600 1.36000 1.36000 1.57000 1.57000 4.00e-25
## 62 1.0700 0.99000 0.99000 1.14000 1.14000 7.70e-02
## 63 1.4900 1.43000 1.43000 1.55000 1.55000 5.60e-68
## 64 1.4200 1.36000 1.36000 1.48000 1.48000 3.83e-60
## 65 1.3400 1.19000 1.19000 1.52000 1.52000 2.88e-06
## 66 1.0400 0.91000 0.91000 1.19000 1.19000 5.48e-01
## 67 1.4300 1.33000 1.33000 1.53000 1.53000 2.56e-23
## 68 1.2900 1.25000 1.25000 1.34000 1.34000 3.38e-55
## 69 1.2800 1.21000 1.21000 1.35000 1.35000 4.50e-09
## 70 1.4000 1.26000 1.26000 1.56000 1.56000 0.00e+00
## 71 1.5500 1.42000 1.42000 1.69000 1.69000 0.00e+00
## 72 1.4300 1.26000 1.26000 1.62000 1.62000 0.00e+00
## 73 1.1000 0.82000 0.82000 1.49000 1.49000 0.00e+00
## 74 1.5500 1.37000 1.37000 1.76000 1.76000 0.00e+00
## 75 1.1100 1.05000 1.05000 1.18000 1.18000 1.32e-02
## 76 1.1400 1.09000 1.09000 1.18000 1.18000 3.12e-11
## 77 1.1300 1.08000 1.08000 1.19000 1.19000 5.95e-08
## 78 1.2300 1.13000 1.13000 1.34000 1.34000 1.48e-05
## 79 1.3700 1.31000 1.31000 1.43000 1.43000 3.41e-05
## 80 1.2100 1.18000 1.18000 1.25000 1.25000 4.38e-35
## 81 1.2300 1.17000 1.17000 1.29000 1.29000 9.33e-18
## 82 1.3000 1.23000 1.23000 1.38000 1.38000 3.38e-11
## 83 1.2100 1.13000 1.13000 1.30000 1.30000 2.89e-03
## 84 1.2500 1.15000 1.15000 1.36000 1.36000 2.44e-06
## 85 1.3000 1.24000 1.24000 1.35000 1.35000 3.47e-31
## 86 1.1200 1.08000 1.08000 1.16000 1.16000 1.27e-11
## 87 1.1400 1.08000 1.08000 1.20000 1.20000 5.61e-03
## 88 1.1400 1.04000 1.04000 1.26000 1.26000 8.20e-03
## 89 1.3100 1.27000 1.27000 1.35000 1.35000 6.03e-68
## 90 1.1100 1.07000 1.07000 1.16000 1.16000 8.18e-07
## 91 0.7400 0.00000 0.00000 0.00000 0.00000 4.00e-22
## 92 -0.5690 0.00000 0.00000 0.00000 0.00000 8.00e-04
## 93 3.0300 2.18000 2.18000 4.22000 4.22000 5.00e-11
## 94 -0.2830 0.00000 0.00000 0.00000 0.00000 7.10e-01
## 95 -1.1090 0.00000 0.00000 0.00000 0.00000 2.00e-06
## 96 -0.5070 0.00000 0.00000 0.00000 0.00000 1.00e-05
## 97 0.4080 0.00000 0.00000 0.00000 0.00000 3.00e-01
## 98 0.6900 0.00000 0.00000 0.00000 0.00000 5.00e-07
## 99 -0.0980 0.00000 0.00000 0.00000 0.00000 8.40e-01
## 100 1.1380 0.00000 0.00000 0.00000 0.00000 2.00e-11
## 101 0.3400 0.21000 0.21000 0.55000 0.55000 1.00e-05
## 102 1.1590 0.00000 0.00000 0.00000 0.00000 6.00e-02
## 103 1.0050 1.00200 1.00200 1.00800 1.00800 1.00e-03
## 104 1.0090 1.00700 1.00700 1.01200 1.01200 1.02e-10
## 105 1.0080 1.00400 1.00400 1.01160 1.01160 1.00e-03
## 106 1.0070 1.00400 1.00400 1.01000 1.01000 5.65e-07
## 107 0.5460 0.31100 0.31100 0.78100 0.78100 5.70e-06
## 108 0.3360 0.18700 0.18700 0.48500 0.48500 9.70e-06
## 109 0.2260 0.14200 0.14200 0.31000 0.31000 1.60e-07
## 110 0.2330 0.13300 0.13300 0.33300 0.33300 4.20e-06
## 111 0.6760 0.43100 0.43100 0.92100 0.92100 6.00e-08
## 112 0.1420 -0.03800 -0.03800 0.32200 0.32200 3.05e-01
## 113 0.2410 -0.07000 -0.07000 0.55200 0.55200 1.30e-01
## 114 0.2050 0.14500 0.14500 0.26500 0.26500 8.30e-12
## 115 0.1580 0.05800 0.05800 0.25800 0.25800 2.00e-02
## 116 0.0480 -0.05000 -0.05000 0.19600 0.19600 5.30e-02
## 117 -0.1610 -0.48200 -0.48200 0.16000 0.16000 3.25e-01
## 118 0.1220 -0.04200 -0.04200 0.28600 0.28600 1.11e-01
## 119 0.2570 0.15700 0.15700 0.35700 0.35700 3.00e-07
## 120 0.3670 0.20200 0.20200 0.53200 0.53200 1.40e-05
## 121 0.2690 -0.01700 -0.01700 0.57600 0.57600 8.70e-02
## 122 0.2960 0.14900 0.14900 0.44300 0.44300 8.30e-05
## 123 0.1900 0.04000 0.04000 0.33900 0.33900 1.20e-02
## 124 0.2480 0.18800 0.18800 0.30800 0.30800 6.80e-17
## 125 0.2670 0.08900 0.08900 0.44500 0.44500 3.00e-03
## 126 -0.0330 -0.34300 -0.34300 0.27700 0.27700 8.34e-01
## 127 0.2840 0.20000 0.20000 0.36800 0.36800 3.10e-11
## 128 0.0550 -0.09800 -0.09800 0.20800 0.20800 4.77e-01
## 129 0.1720 0.07200 0.07200 0.27200 0.27200 1.00e-03
## 130 1.7700 1.53000 1.53000 2.04000 2.04000 6.30e-15
## 131 0.3400 0.21000 0.21000 0.55000 0.55000 1.00e-04
## 132 2.1800 1.80000 1.80000 2.64000 2.64000 2.00e-11
## 133 3.0300 2.18000 2.18000 4.22000 4.22000 2.00e-07
## 134 2.0100 1.79000 1.79000 2.26000 2.26000 6.00e-29
## 135 0.1450 0.11600 0.11600 0.17300 0.17300 4.20e-22
## 136 11.4600 0.00000 0.00000 0.00000 0.00000 1.49e-50
## 137 6.9940 0.00000 0.00000 0.00000 0.00000 9.34e-18
## 138 14.3740 0.00000 0.00000 0.00000 0.00000 4.23e-158
## 139 7.9080 0.00000 0.00000 0.00000 0.00000 3.87e-46
## 140 2.2350 0.00000 0.00000 0.00000 0.00000 1.58e-04
## 141 7.8690 0.00000 0.00000 0.00000 0.00000 2.17e-87
## 142 1.8900 1.37000 1.37000 2.61000 2.61000 0.00e+00
## 143 1.9500 1.35000 1.35000 2.82000 2.82000 3.84e-03
## 144 1.4900 1.24000 1.24000 1.79000 1.79000 2.45e-05
## 145 1.3900 1.21000 1.21000 1.59000 1.59000 2.46e-06
## 146 0.1300 0.00000 0.00000 0.00000 0.00000 1.00e-03
## 147 0.0340 0.00000 0.00000 0.00000 0.00000 9.00e-03
## exposureid.x se
## 1 E1 0.000
## 2 E1 0.000
## 3 E1 0.000
## 4 E1 0.000
## 5 E1 0.000
## 6 E1 0.000
## 7 E1 0.000
## 8 E1 0.000
## 9 E1 0.000
## 10 E1 0.000
## 11 E1 0.000
## 12 E1 0.000
## 13 E1 0.000
## 14 E1 0.000
## 15 E1 0.000
## 16 E1 0.000
## 17 E1 0.000
## 18 E1 0.000
## 19 E1 0.000
## 20 E1 0.000
## 21 E1 0.000
## 22 E1 0.000
## 23 E1 0.000
## 24 E1 0.000
## 25 E1 0.000
## 26 E1 0.000
## 27 E1 0.062
## 28 E1 0.001
## 29 E1 0.031
## 30 E1 0.154
## 31 E1 0.000
## 32 E1 0.000
## 33 E1 0.000
## 34 E1 0.000
## 35 E1 0.000
## 36 E1 0.000
## 37 E1 0.000
## 38 E1 0.000
## 39 E1 0.000
## 40 E1 0.000
## 41 E1 0.000
## 42 E1 0.016
## 43 E2 0.000
## 44 E2 0.000
## 45 E1 0.000
## 46 E1 0.000
## 47 E1 0.000
## 48 E1 0.000
## 49 E1 0.000
## 50 E1 0.000
## 51 E1 0.000
## 52 E1 0.000
## 53 E1 0.000
## 54 E1 0.000
## 55 E1 0.000
## 56 E1 0.000
## 57 E1 0.000
## 58 E1 0.000
## 59 E1 0.400
## 60 E1 0.000
## 61 E1 0.000
## 62 E1 0.000
## 63 E1 0.000
## 64 E1 0.000
## 65 E1 0.000
## 66 E1 0.000
## 67 E1 0.000
## 68 E1 0.000
## 69 E1 0.000
## 70 E1 0.000
## 71 E1 0.000
## 72 E1 0.000
## 73 E1 0.000
## 74 E1 0.000
## 75 E1 0.000
## 76 E1 0.000
## 77 E1 0.000
## 78 E1 0.000
## 79 E1 0.000
## 80 E1 0.000
## 81 E1 0.000
## 82 E1 0.000
## 83 E1 0.000
## 84 E1 0.000
## 85 E1 0.000
## 86 E1 0.000
## 87 E1 0.000
## 88 E1 0.000
## 89 E1 0.000
## 90 E1 0.000
## 91 E1 0.076
## 92 E1 0.155
## 93 E1 0.000
## 94 E1 0.745
## 95 E1 0.231
## 96 E1 0.115
## 97 E1 0.392
## 98 E1 0.113
## 99 E1 0.486
## 100 E1 0.170
## 101 E1 0.000
## 102 E1 0.593
## 103 E1 0.000
## 104 E1 0.000
## 105 E1 0.000
## 106 E1 0.000
## 107 E2 0.000
## 108 E1 0.000
## 109 E1 0.000
## 110 E1 0.000
## 111 E2 0.000
## 112 E1 0.000
## 113 E1 0.000
## 114 E1 0.000
## 115 E1 0.000
## 116 E2 0.000
## 117 E1 0.000
## 118 E1 0.000
## 119 E1 0.000
## 120 E2 0.000
## 121 E1 0.000
## 122 E1 0.000
## 123 E1 0.000
## 124 E1 0.000
## 125 E1 0.000
## 126 E1 0.000
## 127 E1 0.000
## 128 E2 0.000
## 129 E1 0.000
## 130 E1 0.000
## 131 E1 0.000
## 132 E1 0.000
## 133 E1 0.000
## 134 E1 0.000
## 135 E1 0.000
## 136 E1 0.753
## 137 E5 0.810
## 138 E1 0.507
## 139 E1 0.546
## 140 E5 0.591
## 141 E1 0.385
## 142 E1 0.000
## 143 E1 0.000
## 144 E1 0.000
## 145 E1 0.000
## 146 E1 0.000
## 147 E1 0.000
## strata effectsizetype
## 1 BETA
## 2 BETA
## 3 BETA
## 4 BETA
## 5 OR
## 6 OR
## 7 BETA
## 8 BETA
## 9 Nonstratified based on genetic score BETA
## 10 Age less than 55 years BETA
## 11 Greater than or equal to 55 years BETA
## 12 Stratify on basis of less than 55 years. BETA
## 13 Stratified on basis of greater than or equal to 55 years BETA
## 14 stratified on the basis of sex; women BETA
## 15 Stratified on basis of sex; women BETA
## 16 BETA
## 17 BETA
## 18 BETA
## 19 MD
## 20 MD
## 21 MD
## 22 BETA
## 23 BETA
## 24 OR
## 25 OR
## 26 OR
## 27 BETA
## 28 BETA
## 29 BETA
## 30 BETA
## 31 OR
## 32 OR
## 33 OR
## 34 OR
## 35 OR
## 36 OR
## 37 OR
## 38 Females OR
## 39 Females OR
## 40 Males OR
## 41 Males OR
## 42 BETA
## 43 BETA
## 44 BETA
## 45 BETA
## 46 BETA
## 47 RiskDifference
## 48 RiskDifference
## 49 RiskDifference
## 50 RiskDifference
## 51 RiskDifference
## 52 RiskDifference
## 53 RiskDifference
## 54 RiskDifference
## 55 RiskDifference
## 56 RiskDifference
## 57 RiskDifference
## 58 RiskDifference
## 59 BETA
## 60 Pooled results of two cohorts(FinniGen and UKB) OR
## 61 FinnGen study-MR-PRESSO OR
## 62 UKB study using MR-Egger OR
## 63 UKB study -MR-PRESSO OR
## 64 UKB study -IVW OR
## 65 FinnGen study -wt median OR
## 66 FinnGen study- MR Egger method OR
## 67 Results for FinnGen study only OR
## 68 UKB study using self reported hypertension OR
## 69 UKB study-weighted median OR
## 70 OR
## 71 OR
## 72 OR
## 73 OR
## 74 OR
## 75 OR
## 76 OR
## 77 OR
## 78 OR
## 79 OR
## 80 OR
## 81 OR
## 82 OR
## 83 OR
## 84 OR
## 85 OR
## 86 OR
## 87 OR
## 88 OR
## 89 OR
## 90 OR
## 91 BETA
## 92 BETA
## 93 OR
## 94 BETA
## 95 BETA
## 96 BETA
## 97 BETA
## 98 BETA
## 99 BETA
## 100 BETA
## 101 OR
## 102 BETA
## 103 OR
## 104 OR
## 105 OR
## 106 OR
## 107 BETA
## 108 BETA
## 109 BETA
## 110 BETA
## 111 BETA
## 112 BETA
## 113 BETA
## 114 BETA
## 115 BETA
## 116 BETA
## 117 BETA
## 118 BETA
## 119 BETA
## 120 BETA
## 121 BETA
## 122 BETA
## 123 BETA
## 124 BETA
## 125 BETA
## 126 BETA
## 127 BETA
## 128 BETA
## 129 BETA
## 130 OR
## 131 OR
## 132 OR
## 133 OR
## 134 OR
## 135 RiskDifference
## 136 BETA
## 137 BETA
## 138 BETA
## 139 BETA
## 140 BETA
## 141 BETA
## 142 OR
## 143 OR
## 144 OR
## 145 OR
## 146 BETA
## 147 BETA
## methodname Exposureid_resultsid exposureid.y exposurename
## 1 TSLS E1_R57 E1 BMI
## 2 TSLS E1_R58 E1 BMI
## 3 IVestimator E1_R3 E1 BMI
## 4 IVestimator E1_R4 E1 BMI
## 5 IVestimator E1_R2 E1 BMI
## 6 IVestimator E1_R1 E1 BMI
## 7 IVestimator E1_R92 E1 BMI
## 8 IVestimator E1_R93 E1 BMI
## 9 IVestimator E1_R77 E1 BMI
## 10 IVestimator E1_R79 E1 BMI
## 11 IVestimator E1_R80 E1 BMI
## 12 IVestimator E1_R81 E1 BMI
## 13 IVestimator E1_R82 E1 BMI
## 14 IVestimator E1_R83 E1 BMI
## 15 IVestimator E1_R84 E1 BMI
## 16 IVestimator E1_R78 E1 BMI
## 17 IVestimator E1_R86 E1 BMI
## 18 IVestimator E1_R85 E1 BMI
## 19 TSLS E1_R7 E1 BMI
## 20 TSLS E1_R5 E1 BMI
## 21 TSLS E1_R6 E1 BMI
## 22 TSLS E1_R44 E1 BMI
## 23 TSLS E1_R43 E1 BMI
## 24 TSLS E1_R42 E1 BMI
## 25 IVestimator E1_R108 E1 BMI
## 26 IVestimator E1_R109 E1 BMI
## 27 MREgger E1_R74 E1 BMI
## 28 Wetmedian E1_R76 E1 BMI
## 29 IVW E1_R73 E1 BMI
## 30 SIMEXcorrectedMREgger E1_R75 E1 BMI
## 31 IVW E1_R59 E1 BMI
## 32 Wetmedian E1_R64 E1 BMI
## 33 MREgger E1_R65 E1 BMI
## 34 MVMR E1_R67 E1 BMI
## 35 IVW E1_R63 E1 BMI
## 36 MVMR E1_R68 E1 BMI
## 37 MR-PRESSO E1_R66 E1 BMI
## 38 TSLS E1_R10 E1 BMI
## 39 IVW E1_R8 E1 BMI
## 40 TSLS E1_R11 E1 BMI
## 41 IVW E1_R9 E1 BMI
## 42 TSLS E1_R41 E1 BMI
## 43 TSLS E2_R123 E2 WHR
## 44 TSLS E2_R121 E2 WHR
## 45 TSLS E1_R122 E1 BMI
## 46 TSLS E1_R120 E1 BMI
## 47 TSLS E1_R15 E1 BMI
## 48 TSLS E1_R14 E1 BMI
## 49 Wetmode E1_R19 E1 BMI
## 50 MREgger E1_R20 E1 BMI
## 51 IVW E1_R21 E1 BMI
## 52 Wetmedian E1_R22 E1 BMI
## 53 IVW E1_R17 E1 BMI
## 54 IVW E1_R16 E1 BMI
## 55 Wetmode E1_R23 E1 BMI
## 56 Wetmedian E1_R18 E1 BMI
## 57 TSLS E1_R13 E1 BMI
## 58 MREgger E1_R24 E1 BMI
## 59 IVW E1_124 E1 BMI
## 60 IVW E1_R47 E1 BMI
## 61 MR-PRESSO E1_R51 E1 BMI
## 62 MREgger E1_R54 E1 BMI
## 63 MR-PRESSO E1_R55 E1 BMI
## 64 IVW E1_R52 E1 BMI
## 65 Wetmedian E1_R49 E1 BMI
## 66 MREgger E1_R50 E1 BMI
## 67 IVW E1_R48 E1 BMI
## 68 IVW E1_R56 E1 BMI
## 69 Wetmedian E1_R53 E1 BMI
## 70 Wetmedian E1_R88 E1 BMI
## 71 MR-PRESSO E1_R91 E1 BMI
## 72 Wetmode E1_R89 E1 BMI
## 73 MREgger E1_R90 E1 BMI
## 74 IVW E1_R87 E1 BMI
## 75 Wetmode E1_R27 E1 BMI
## 76 IVW E1_R29 E1 BMI
## 77 Wetmedian E1_R30 E1 BMI
## 78 Wetmode E1_R35 E1 BMI
## 79 MREgger E1_R32 E1 BMI
## 80 MVMR E1_R33 E1 BMI
## 81 Wetmedian E1_R34 E1 BMI
## 82 Wetmode E1_R39 E1 BMI
## 83 MREgger E1_R28 E1 BMI
## 84 MREgger E1_R40 E1 BMI
## 85 Wetmedian E1_R38 E1 BMI
## 86 IVW E1_R25 E1 BMI
## 87 Wetmode E1_R31 E1 BMI
## 88 MREgger E1_R36 E1 BMI
## 89 IVW E1_R37 E1 BMI
## 90 Wetmedian E1_R26 E1 BMI
## 91 Wetmedian E1_R101 E1 BMI
## 92 IVW E1_R99 E1 BMI
## 93 IVW E1_R96 E1 BMI
## 94 MREgger E1_R106 E1 BMI
## 95 Wetmedian E1_R107 E1 BMI
## 96 Wetmedian E1_R103 E1 BMI
## 97 MREgger E1_R100 E1 BMI
## 98 IVW E1_R98 E1 BMI
## 99 MREgger E1_R102 E1 BMI
## 100 Wetmedian E1_R105 E1 BMI
## 101 IVW E1_R97 E1 BMI
## 102 MREgger E1_R104 E1 BMI
## 103 IVW E1_R112 E1 BMI
## 104 Wetmedian E1_R111 E1 BMI
## 105 IVW E1_R110 E1 BMI
## 106 Wetmedian E1_R113 E1 BMI
## 107 TSLS E2_R130 E2 WHR
## 108 TSLS E1_R128 E1 BMI
## 109 TSLS E1_R132 E1 BMI
## 110 TSLS E1_R125 E1 BMI
## 111 TSLS E2_R138 E2 WHR
## 112 TSLS E1_R131 E1 BMI
## 113 TSLS E1_R127 E1 BMI
## 114 TSLS E1_R129 E1 BMI
## 115 TSLS E1_R141 E1 BMI
## 116 TSLS E2_R142 E2 WHR
## 117 TSLS E1_R143 E1 BMI
## 118 TSLS E1_R144 E1 BMI
## 119 TSLS E1_R133 E1 BMI
## 120 TSLS E2_R134 E2 WHR
## 121 TSLS E1_R135 E1 BMI
## 122 TSLS E1_R136 E1 BMI
## 123 TSLS E1_R148 E1 BMI
## 124 TSLS E1_R137 E1 BMI
## 125 TSLS E1_R139 E1 BMI
## 126 TSLS E1_147 E1 BMI
## 127 TSLS E1_140 E1 BMI
## 128 TSLS E2_R146 E2 BMI
## 129 TSLS E1_R145 E1 BMI
## 130 IVW E1_R95 E1 BMI
## 131 IVW E1_R71 E1 BMI
## 132 IVW E1_R69 E1 BMI
## 133 IVW E1_R72 E1 BMI
## 134 IVW E1_R70 E1 BMI
## 135 TSLS E1_R12 E1 BMI
## 136 TSLS E1_R116 E1 BMI
## 137 TSLS E1_R115 E5 WHtR
## 138 TSLS E1_R114 E1 BMI
## 139 TSLS E1_R119 E1 BMI
## 140 TSLS E5_R118 E5 WHtR
## 141 TSLS E1_R117 E1 BMI
## 142 IVW E1_R94 E1 BMI
## 143 MREgger E1_R62 E1 BMI
## 144 Wetmedian E1_R61 E1 BMI
## 145 IVW E1_R60 E1 BMI
## 146 TSLS E1_R45 E1 BMI
## 147 MRGXE E1_R46 E1 BMI
## exposuremeasured
## 1 weight divided by height in square metres
## 2 weight divided by height in square metres
## 3 weight divided by height in square metres
## 4 weight divided by height in square metres
## 5 weight divided by height in square metres
## 6 weight divided by height in square metres
## 7 weight divided by height in square metres
## 8 weight divided by height in square metres
## 9 weight divided by height in square metres
## 10 weight divided by height in square metres
## 11 weight divided by height in square metres
## 12 weight divided by height in square metres
## 13 weight divided by height in square metres
## 14 weight divided by height in square metres
## 15 weight divided by height in square metres
## 16 weight divided by height in square metres
## 17 weight divided by height in square metres
## 18 weight divided by height in square metres
## 19 weight divided by height in square metres (evaluated as log BMI)
## 20 weight divided by height in square metres
## 21 weight divided by height in square metres (evaluated as log BMI)
## 22 weight divided by height in square metres
## 23 weight divided by height in square metres
## 24 weight divided by height in square metres
## 25 weight divided by height in square metres
## 26 weight divided by height in square metres
## 27 weight divided by height in square metres
## 28 weight divided by height in square metres
## 29 weight divided by height in square metres
## 30 weight divided by height in square metres
## 31 weight divided by height in square metres
## 32 weight divided by height in square metres
## 33 weight divided by height in square metres
## 34 Assessed using bioelectrical impedance technique. Fat mass index divided by height squared.
## 35 weight divided by height in square metres
## 36 assessed using bioelectric impedance
## 37 weight divided by height in square metres
## 38 Measured using dual energy X-ray absorptiometry
## 39 Measured using dual energy X-ray absorptiometry
## 40 Measured using dual energy X-ray absorptiometry
## 41 Measured using dual energy X-ray absorptiometry
## 42 weight divided by height I square metres
## 43 waist circumference divided by hip circumference
## 44 waist circumference divided by hip circumference
## 45 weight divided by height in square metres
## 46 weight divided by height in square metres
## 47 weight divided by height in square metres
## 48 weight divided by height in square metres
## 49 weight divided by height in square metres
## 50 weight divided by height in square metres
## 51 weight divided by height in square metres
## 52 weight divided by height in square metres
## 53 weight divided by height in square metres
## 54 weight divided by height in square metres
## 55 weight divided by height in square metres
## 56 weight divided by height in square metres
## 57 weight divided by height in square metres
## 58 weight divided by height in square metres
## 59 weight divided by height in square metres
## 60 weight divided by height in square metres
## 61 weight divided by height in square metres
## 62 weight divided by height in square metres
## 63 weight divided by height in square metres
## 64 weight divided by height in square metres
## 65 weight divided by height in square metres
## 66 weight divided by height in square metres
## 67 weight divided by height in square metres
## 68 weight divided by height in square metres
## 69 weight divided by height in square metres
## 70 weight divided by height in square metres
## 71 weight divided by height in square metres
## 72 weight divided by height in square metres
## 73 weight divided by height in square metres
## 74 weight divided by height in square metres
## 75 weight divided by height in square metres
## 76 weight divided by heigh in square metres
## 77 weight divided by height in square metres
## 78 weight divided by height in square metres
## 79 weight divided by height in square metres
## 80 weight divided by height in square metres
## 81 weight divided by height in square metres
## 82 weight divided by height in square metres
## 83 weight divided by height in square metres
## 84 weight divided by height in square metres
## 85 weight divided by height in square metres
## 86 Weight divided by heigh in square metres
## 87 weight divided by height in square metres
## 88 weight divided by height in square metres
## 89 weight divided by height I square metres
## 90 weight divided by height in square metres
## 91 MRI scan on body fat percentage
## 92 MRI scan of body fat percentage-favourable adiposity
## 93 MRI scan to measure body fat percentage
## 94 MRI scan of body fat percentage
## 95 MRI scan of body fat percentage-FA
## 96 MRI scan of body fat percentage
## 97 MRI scan of body fat percentage
## 98 MRI scan for body fat percentage
## 99 MRI scan for body fat percentage
## 100 MRI scan of body fat percentage
## 101 MRI scan of body fat percentage
## 102 MRI scan of body fat percentage
## 103 weight divided by height in square metres
## 104 weight divided by height in square metres
## 105 weight divided by height in square metres
## 106 weight divided by height in square metres
## 107 waistband hip circumference
## 108 MRI scan of VAT from original GWAS (UKB)
## 109 weight divided by height in square metres
## 110 weight divided by height in square metres
## 111 waist and hip circumference ratio
## 112 Bioelectric impedance analysers
## 113 Bioelectrical impedance analyser
## 114 weight divided by height in square metres
## 115 weight divided by height in square metres
## 116 waist and hip circumference ratio
## 117 weight divided by height in square metres
## 118 weight divided by height in square metres
## 119 weight divided by height in square metres
## 120 waist and hip circumference ratio
## 121 bioelectric impedance analyser
## 122 MRI scan for VAT in UKBiobank for original GWAS
## 123 MRI scan in UKB for VAT
## 124 weight divided by height in square metres
## 125 Bioelectric impedance
## 126 Bioelectric impedance
## 127 MR scan of VAT in UKB
## 128 waist and hip circumference ratio
## 129 weight divided by height in square metres
## 130 weight divided by height in square metres
## 131 Bioimpedance measurements
## 132 weight divided by height in square metres
## 133 Bioimpedance
## 134 Bioimpedance measures of body fat percentage
## 135 weight divided by height in square metres. Height measure to the nearest centimetre using a Seca 202 device and weight to the nearest 0.1 kg using Tanita BC418MA body composition analyzer.
## 136 Bioelectric impedance(fat mass percentage)
## 137 waist circumference divided by height(WHtR)
## 138 weight divided by height in square metres
## 139 Bioimpedance -fat mass percentage
## 140 waist circumference divided by height
## 141 weight divided by height in square metres
## 142 weight divided by height in square metres
## 143 weight divided by height in square metres
## 144 weight divided by height in square metres
## 145 weight divided by height in square metres
## 146 weight divided by height in square metres
## 147 weight divided by height in square metres
## exposurenotes outcomeid_resultsid outcomeid.y outcomename
## 1 Adult O2_R57 O2 SBP
## 2 Adult O3_R58 O3 DBP
## 3 Adult 03_R3 O3 DBP
## 4 Adult 02_R4 O2 SBP
## 5 Adult O1_R2 O1 hypertension
## 6 Adult O1_R1 O1 hypertension
## 7 Adult O2_R92 O2 SBP
## 8 Adult O3_R93 O3 DBP
## 9 Adult O3_R77 O3 DBP
## 10 Adult 03_R79 O3 DBP
## 11 Adult O3_R80 O3 DBP
## 12 Adult 02_R81 O2 SBP
## 13 Adult O2_R82 O2 SBP
## 14 Adult O3_R83 O3 DBP
## 15 Adult O2_R84 O2 SBP
## 16 Adult O2_R78 O2 SBP
## 17 Adult O2_R86 O2 SBP
## 18 Adult O3_R85 O3 DBP
## 19 Childhood O2_R7 O2 SBP
## 20 Childhood 02_R5 O2 SBP
## 21 Childhood O2_R6 O2 SBP
## 22 Adult O3_R44 O3 DBP
## 23 Adult O2_R43 O2 SBP
## 24 Adult O1_R42 O1 hypertension
## 25 Adult O1_R108 O1 hypertension
## 26 Adult 01_R109 O1 hypertension
## 27 Adult O2_R74 O2 SBP
## 28 Adult O2_R76 O2 SBP
## 29 Adult O2_R73 O2 SBP
## 30 Adult O2_R75 O2 SBP
## 31 Adult O1_R59 O1 hypertension
## 32 Adult O1_R64 O1 hypertension
## 33 Adult O1_R65 O1 hypertension
## 34 Adult 01_R67 O1 hypertension
## 35 Adult O1_R63 O1 hypertension
## 36 Adult O1_R68 O1 hypertension
## 37 Adult O1_R66 O1 hypertension
## 38 Adult O1_R10 O1 hypertension
## 39 Adult O1_R8 O1 hypertension
## 40 Adult 01_R11 O1 hypertension
## 41 Adult O1_R9 O1 hypertension
## 42 Adult O3_R41 O3 DBP
## 43 Childhood 03_R123 O3 DBP
## 44 Childhood O2_R121 O2 SBP
## 45 Childhood O3_R122 O3 DBP
## 46 Childhood O2_R120 O2 SBP
## 47 Adult O1_R15 O1 hypertension
## 48 Adult O1_R14 O1 hypertension
## 49 Adult O1_R19 O1 hypertension
## 50 Adult O1_R20 O1 hypertension
## 51 Adult O1_R21 O1 hypertension
## 52 Adult O1_R22 O1 hypertension
## 53 Adult O1_R17 O1 hypertension
## 54 Adult O1_R16 O1 hypertension
## 55 Adult O1_R23 O1 hypertension
## 56 Adult O1_R18 O1 hypertension
## 57 Adult O1_R13 O1 hypertension
## 58 Adult O1_R24 O1 hypertension
## 59 Adult O4_R124 O4 PAP
## 60 Adult O1_R47 O1 hypertension
## 61 Adult O1_R51 O1 hypertension
## 62 Adult O1_R54 O1 hypertension
## 63 Adult O1_R55 O1 hypertension
## 64 Adult O1_R52 O1 hypertension
## 65 Adult O1_R49 O1 hypertension
## 66 Adult O1_R50 O1 hypertension
## 67 Adult O1_R48 O1 hypertension
## 68 Adult O1_R56 O1 hypertension
## 69 Adult O1_R53 O1 hypertension
## 70 Adult O1_R88 O1 hypertension
## 71 Adult O1_R91 O1 hypertension
## 72 Adult O1_R89 O1 hypertension
## 73 Adult O1_R90 O1 hypertension
## 74 Adult O1_R87 O1 hypertension
## 75 Childhood O1_R27 O1 hypertension
## 76 Childhood O1_R29 O1 hypertension
## 77 Childhood O1_R30 O1 hypertension
## 78 Adult O1_R35 O1 hypertension
## 79 Childhood O1_R32 O1 hypertension
## 80 Adult O1_R33 O1 hypertension
## 81 Adult O1_R34 O1 hypertension
## 82 Adult O1_R39 O1 hypertension
## 83 Childhood O1_R28 O1 hypertension
## 84 Adult O1_R40 O1 hypertension
## 85 Adult O1_R38 O1 hypertension
## 86 Childhood O1_R25 O1 hypertension
## 87 Childhood O1_R31 O1 hypertension
## 88 Adult O1_R36 O1 hypertension
## 89 Adult O1_R37 O1 hypertension
## 90 Childhood 01_R26 O1 hypertension
## 91 Adult O1_R101 O1 hypertension
## 92 Adult O1_R99 O1 hypertension
## 93 Adult O1_R96 O1 hypertension
## 94 Adult O1_R106 O1 hypertension
## 95 Adult O1_R107 O1 hypertension
## 96 Adult O1_R103 O1 hypertension
## 97 Adult O1_R100 O1 hypertension
## 98 Adult O1_R98 O1 hypertension
## 99 Adult O1_R102 O1 hypertension
## 100 Adult O1_R105 O1 hypertension
## 101 Adult O1_R97 O1 hypertension
## 102 Adult O1_R104 O1 hypertension
## 103 Childhood O1_R112 O1 hypertension
## 104 Childhood O1_R111 O1 hypertension
## 105 Childhood O1_R110 O1 hypertension
## 106 Childhood O1_R113 O1 hypertension
## 107 Adult O2_R130 O2 SBP
## 108 Adult O2_R128 O2 SBP
## 109 Adult O2_R132 O2 SBP
## 110 Adult O2_R125 O2 SBP
## 111 Adult O3_R138 O3 DBP
## 112 Adult O2_R131 O2 SBP
## 113 Adult O2_R127 O2 SBP
## 114 Adult O2_R129 O2 SBP
## 115 Adult O2_R141 O2 SBP
## 116 Adult 02_R142 O2 SBP
## 117 Adult O2_R143 O2 SBP
## 118 Adult O2_R144 O2 SBP
## 119 Adult O3_R133 O3 DBP
## 120 Adult O3_R134 O3 DBP
## 121 Adult O3_R135 O3 DBP
## 122 Adult O3_R136 O3 DBP
## 123 Adult O3_R148 O3 DBP
## 124 Adult O3_R137 O3 DBP
## 125 Adult O3_R139 O3 DBP
## 126 Adult O3_R147 O3 DBP
## 127 Adult O3_R140 O3 DBP
## 128 Adult O3_R146 O3 DBP
## 129 Adult O3_R145 O3 DBP
## 130 Childhood O1_R95 O1 hypertension
## 131 Adult O1_R71 O1 hypertension
## 132 Adult O1_R69 O1 hypertension
## 133 Adult O1_R72 O1 hypertension
## 134 Adult O1_R70 O1 hypertension
## 135 Adult O3_R12 O3 DBP
## 136 Childhood O2_R116 O2 SBP
## 137 Childhood O2_R115 O2 SBP
## 138 Childhood O2_R114 O2 SBP
## 139 Childhood O3_R119 O3 DBP
## 140 Childhood O3_R118 O3 DBP
## 141 Childhood O3_R117 O3 DBP
## 142 Adult 06_R94 O6 Grade1diastolicdysfunction
## 143 Adult O1_R62 O1 hypertension
## 144 Adult O1_R61 O1 hypertension
## 145 Adult O1_R60 O1 hypertension
## 146 Adult O2_R45 O2 SBP
## 147 Adult O2_R46 O2 SBP
## outcomemeasured
## 1 Measured using automatic digital blood pressure monitor moderated elevated BP(SBP > 140mm Hg)
## 2 Measured using automatic digital blood pressure monitor elevated DBP >90mm Hg
## 3 Self reporting, biochemical measurement, health registry and medical records
## 4 self reported, biochemical measurement, health registry and medical records
## 5 self reported, biochemical measurement, health registry and medical records
## 6 Self reported, biochemical measurement, health registry and medical records
## 7
## 8
## 9
## 10
## 11
## 12
## 13
## 14
## 15
## 16
## 17
## 18
## 19 ALSPAC (mm Hg)
## 20 ALSPAC (mm Hg)
## 21 ALSPAC (mm Hg)
## 22 digital blood pressure monitors
## 23 Using digital blood pressure monitor
## 24 Self reporting use of antihypertensive medication and having received doctor diagnosis
## 25 BP measured using mercury sphygmanometers (hypertensive SBP>140 or >90mm Hg for DBP) using antihypertensive within the 10 year followup
## 26 systolic /diastolic greater than 140mm Hg and 90 mm Hg respectively and use of medication
## 27
## 28
## 29
## 30
## 31 hypertension as defined in UKBiobank
## 32 ICD 9 and 10, self reporting(doctors diagnosis
## 33 ICD 9 and 10, self diagnosis(doctor diagnosis)
## 34 ICD 9 and 10, self reporting(doctor diagnosis)
## 35 arterial hypertension aș assessed in UKBiobank
## 36 ICD 9 and 10, self reporting (Doctor diagnosis)
## 37 ICD 9 and 10, self diagnosis(doctor diagnosis)
## 38
## 39
## 40
## 41
## 42
## 43
## 44
## 45
## 46
## 47 Self reported high blood pressure with current antihypertensive medication and SBP/DBP greater than 140/90 mm Hg.
## 48 self reported high blood pressure defined as either currently taking medication or SBP /DBP greater than 140/90 mm Hg respectively
## 49 Self reporting of high blood pressure , current use of medication or SBP/DBP greater than 140/90 mm Hg respectively
## 50 Self reporting of high blood pressure; current medication or SBP/DBP greater than 140/90 mm Hg
## 51 Self reporting of high blood pressure with medication use or SBP/DBP greater than 140/90 mm Hg
## 52 self reporting of high blood pressure
## 53 self reported high blood pressure , current antihypertensive medication or SBP/DBP greater than 140/90 mm Hg
## 54 Self reported with current use of medication or SBP/DBP greater than 140/90 mm Hg
## 55 Self reporting of high blood pressure
## 56 self reporting defined as current use of antihypertensive medication or SBP/DBP greater than 140/90 mm Hg respectively
## 57 Self reported high blood pressure defined as either currently taking anti-hypertensive medication ad having systolic or diastolic blood pressure above 140mm Hg or 90 mm Hg respectively.
## 58 Self reporting of high blood pressure
## 59
## 60 ICD 10 from discharge registries
## 61 I10 diagnosis in discharge registries
## 62 I10 diagnosis of essential hypertension
## 63 I10 diagnosis of essential hypertension
## 64 I10 diagnosis for essential hypertension
## 65 I10 diagnosis , discharge registries
## 66 I10 diagnosis in discharge registries
## 67 I10 diagnosis in discharge registries
## 68 self reported hypertension
## 69 I10 diagnosis of essential hypertension
## 70
## 71
## 72
## 73
## 74
## 75 I10 Essential hypertension
## 76 Vascular/heart problems diagnosed by doctor: High blood pressure
## 77 Vascular/heart problems diagnosed by doctor: High blood pressure
## 78
## 79 Vascular/heart problems diagnosed by doctor: high blood pressure
## 80
## 81
## 82 Vascular/heart problems diagnosed by doctor:high blood pressure
## 83 I10 Essential hypertension
## 84 Vascular/heart problems diagnosed by doctor:high blood pressure
## 85 Vascular/heart problems diagnosed by doctor: high blood pressure
## 86 I10 Essential primary hypertension
## 87 Vascular/heart problems diagnosed by doctor:high blood pressure
## 88
## 89 Vascular/heart problems diagnosed by doctor:high blood pressure
## 90 I10 Essential hypertension
## 91
## 92
## 93
## 94
## 95
## 96
## 97
## 98
## 99
## 100
## 101
## 102
## 103
## 104
## 105
## 106
## 107
## 108
## 109
## 110 Manual measurement of SBP at baseline
## 111
## 112
## 113
## 114
## 115
## 116
## 117
## 118
## 119
## 120
## 121
## 122
## 123
## 124
## 125
## 126
## 127
## 128
## 129
## 130
## 131
## 132
## 133
## 134
## 135 An automated reading form an Moron blood pressure monitor
## 136
## 137
## 138
## 139
## 140
## 141
## 142 Transthoracic echocardiography
## 143
## 144
## 145 Fingenn study description
## 146 Digital blood pressure monitors
## 147 Digital blood pressure monitors
## totalsamplesize_outcome X.cases_outcome control_outcome
## 1 37011 0 0
## 2 37010 0 0
## 3 130380 0 0
## 4 147644 0 0
## 5 737 600 137
## 6 155191 56271 98470
## 7 30136 0 0
## 8 30137 0 0
## 9 0 0 0
## 10 35518 0 0
## 11 32596 0 0
## 12 35681 0 0
## 13 32989 0 0
## 14 34333 0 0
## 15 34459 0 0
## 16 117781 0 0
## 17 33094 0 0
## 18 32664 0 0
## 19 4641 0 0
## 20 4641 0 0
## 21 4641 0 0
## 22 111638 0 0
## 23 111637 0 0
## 24 32874 0 0
## 25 8832 4380 4452
## 26 8832 4380 4452
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 367703 119500 248203
## 33 367703 119500 248203
## 34 0 0 0
## 35 367703 119500 248203
## 36 0 0 0
## 37 367703 119500 248203
## 38 0 0 0
## 39 110585 25414 85171
## 40 0 0 0
## 41 109960 33744 76216
## 42 105276 0 0
## 43 2030 0 0
## 44 2030 0 0
## 45 2030 0 0
## 46 2030 0 0
## 47 61008 0 0
## 48 61008 0 0
## 49 61008 0 0
## 50 61008 0 0
## 51 223368 0 0
## 52 223368 0 0
## 53 61008 0 0
## 54 61008 0 0
## 55 223368 0 0
## 56 61008 0 0
## 57 354836 0 0
## 58 223368 0 0
## 59 0 0 0
## 60 553225 70228 482997
## 61 90215 15870 74345
## 62 463010 54358 408652
## 63 463010 54358 408652
## 64 463010 54358 408652
## 65 90215 15870 74345
## 66 90215 15870 74345
## 67 90215 15870 74345
## 68 542933 199731 343202
## 69 463010 54358 408652
## 70 332382 62802 269580
## 71 332382 62802 269580
## 72 332382 62802 269580
## 73 332382 62802 269580
## 74 0 0 0
## 75 0 0 0
## 76 0 0 0
## 77 0 0 0
## 78 0 0 0
## 79 0 0 0
## 80 0 0 0
## 81 0 0 0
## 82 0 0 0
## 83 0 0 0
## 84 0 0 0
## 85 0 0 0
## 86 0 0 0
## 87 0 0 0
## 88 0 0 0
## 89 0 0 0
## 90 0 0 0
## 91 451025 101426 349599
## 92 451025 101426 349599
## 93 140790 43576 97214
## 94 140790 43576 97214
## 95 140790 43576 97214
## 96 451025 101426 349599
## 97 451025 101426 349599
## 98 451025 101426 349599
## 99 451025 101426 349599
## 100 140790 43576 97214
## 101 140790 43576 97214
## 102 140790 43576 97214
## 103 463010 54358 408652
## 104 463010 54358 408652
## 105 463010 54358 408652
## 106 463010 54358 408652
## 107 29247 0 0
## 108 9140 0 0
## 109 29247 0 0
## 110 9140 0 0
## 111 29247 0 0
## 112 29274 0 0
## 113 9140 0 0
## 114 29247 0 0
## 115 9041 0 0
## 116 9041 0 0
## 117 9041 0 0
## 118 9041 0 0
## 119 9140 0 0
## 120 9140 0 0
## 121 9140 0 0
## 122 9140 0 0
## 123 9041 0 0
## 124 29247 0 0
## 125 29247 0 0
## 126 9041 0 0
## 127 29247 0 0
## 128 9041 0 0
## 129 9041 0 0
## 130 0 0 0
## 131 0 0 0
## 132 0 0 0
## 133 0 0 0
## 134 0 0 0
## 135 0 0 0
## 136 3266 0 0
## 137 3266 0 0
## 138 3266 0 0
## 139 3266 0 0
## 140 3266 0 0
## 141 3266 0 0
## 142 2440 668 1772
## 143 96499 3363 93136
## 144 96499 3363 93136
## 145 96499 3363 93136
## 146 0 0 0
## 147 0 0 0
## outcomenotes notesid no_ofIVs
## 1 Systolic blood pressure N57 2
## 2 Diastolic blood pressure N58 2
## 3 diastolic blood pressure in mm Hg N3 1
## 4 systolic blood pressure in mm Hg N4 1
## 5 Incident hypertension N2 1
## 6 Ever hypertension N1 1
## 7 Systolic blood pressure N92 14
## 8 Diastolic blood pressure N93 14
## 9 Diastolic blood pressure N77 32
## 10 Diastolic blood pressure N79 32
## 11 Diastolic blood pressure N80 32
## 12 Systolic blood pressure N81 32
## 13 Systolic blood pressure N82 32
## 14 Diastolic blood pressure N83 32
## 15 Systolic blood pressure N84 32
## 16 Systolic blood pressure N78 32
## 17 Systolic blood pressure N86 32
## 18 Diastolic blood pressure N85 32
## 19 systolic blood pressure not log transformed N7 1
## 20 systolic blood pressure not log transformed N5 32
## 21 systolic blood pressure not log transformed N6 31
## 22 Diastolic blood pressure N44 93
## 23 Systolic blood pressure N43 93
## 24 Hypertension N42 93
## 25 Hypertension N108 2
## 26 Hypertension N109 2
## 27 Systolic blood pressure N74 96
## 28 Systolic blood pressure N76 96
## 29 Systolic blood pressure N73 96
## 30 Systolic blood pressure N75 96
## 31 Hypertension N59 97
## 32 Arterial hypertension N64 96
## 33 Arterial hypertension N65 96
## 34 Arterial hypertension N67 82
## 35 Arterial hypertension N63 96
## 36 Arterial hypertension N68 82
## 37 Arterial hypertension N66 96
## 38 Hypertension N10 44
## 39 Hypertension N8 44
## 40 Hypertension N11 44
## 41 hypertension N9 44
## 42 Diastolic blood pressure N41 96
## 43 Diastolic blood pressure N123 5
## 44 Systolic blood pressure N121 5
## 45 Diastolic blood pressure N122 5
## 46 Systolic blood pressure N120 5
## 47 High blood pressure N15 79
## 48 High blood pressure N14 79
## 49 High blood pressure N19 79
## 50 High blood pressure N20 79
## 51 High blood pressure N21 64
## 52 High blood pressure N22 64
## 53 High blood pressure N17 79
## 54 High blood pressure N16 79
## 55 High blood pressure N23 64
## 56 High blood pressure N18 79
## 57 High blood pressure N13 79
## 58 High blood pressure N24 64
## 59 Pulmonary arterial hypertension N124 64
## 60 Essential(primary) hypertension N47 812
## 61 Essential(primary)hypertension N51 810
## 62 Essential(primary)hypertension N54 832
## 63 Essential(primary)hypertension N55 816
## 64 essential(primary)hypertension N52 832
## 65 Essential(primary) hypertension N49 812
## 66 Essential(primary) hypertension N50 812
## 67 Essential(primary) hypertension N48 812
## 68 Hypertension N56 832
## 69 Essential(primary)hypertension N53 832
## 70 N88 76
## 71 Hypertension N91 59
## 72 Hypertension N89 76
## 73 Hypertension N90 76
## 74 Hypertension N87 76
## 75 Essential hypertension N27 13
## 76 High blood pressure N29 13
## 77 High blood pressure N30 13
## 78 I10 Essential hypertension N35 76
## 79 High blood pressure N32 13
## 80 I10 Essential hypertension N33 76
## 81 I10 Essential hypertension N34 76
## 82 High blood pressure N39 76
## 83 Essential hypertension N28 13
## 84 High blood pressure N40 76
## 85 High blood pressure N38 76
## 86 Essential hypertension N25 13
## 87 High blood pressure N31 13
## 88 I10 essential hypertension N36 76
## 89 High blood pressure N37 76
## 90 Essential hypertension N26 13
## 91 Hypertension N101 38
## 92 Hypertension N99 36
## 93 Hypertension N96 38
## 94 Hypertension N106 36
## 95 Hypertension N107 36
## 96 Hypertension N103 36
## 97 Hypertension N100 38
## 98 Hypertension N98 38
## 99 Hypertension N102 36
## 100 Hypertension N105 38
## 101 Hypertension N97 36
## 102 Hypertension N104 38
## 103 Hypertension N112 16
## 104 Hypertension N111 7
## 105 hypertension N110 7
## 106 Hypertension N113 16
## 107 Systolic blood pressure N130 324
## 108 Systolic blood pressure N128 208
## 109 Systolic blood pressure N132 208
## 110 Systolic blood pressure N125 565
## 111 Diastolic blood pressure N138 324
## 112 Systolic blood pressure N131 81
## 113 Systolic blood pressure N127 81
## 114 Systolic blood pressure N129 565
## 115 Systolic blood pressure N141 565
## 116 Systolic blood pressure N142 324
## 117 Systolic blood pressure N143 81
## 118 Systolic blood pressure N144 208
## 119 Diastolic blood pressure N133 565
## 120 Diastolic blood pressure N134 324
## 121 Diastolic blood pressure N135 81
## 122 Diastolic blood pressure N136 208
## 123 Diastolic blood pressure N148 208
## 124 Diastolic blood pressure N137 565
## 125 Diastolic blood pressure N139 81
## 126 Diastolic blood pressure N147 81
## 127 Diastolic blood pressure N140 208
## 128 Diastolic blood pressure N146 324
## 129 Diastolic blood pressure N145 565
## 130 Hypertension N95 295
## 131 Hypertension N71 36
## 132 Hypertension N69 73
## 133 Hypertension N72 38
## 134 Hypertension N70 696
## 135 Diastolic blood pressure N12 93
## 136 Systolic blood pressure N116 4
## 137 Systolic blood pressure N115 4
## 138 Systolic blood pressure N114 4
## 139 Diastolic blood pressure N119 4
## 140 Diastolic blood pressure N118 4
## 141 Diastolic blood pressure N117 4
## 142 Grade 1 Diastolic Dysfunction N94 0
## 143 Gestational hypertension N62 14
## 144 Gestational hypertension N61 14
## 145 Gestational hypertension N60 14
## 146 Systolic blood pressure N45 95
## 147 Systolic blood pressure N46 95
## unitsofmeasurement
## 1 SBP estimated increase in mm Hg for each 10% increase in BMI
## 2 DBP estimated increase in mm Hg for each 10% increase in BMI
## 3 one unit increase in BMI (kg/m2)
## 4 one unit increase in BMI (kg/m2)
## 5 one unit increase in BMI (kg/m2)
## 6 one unit increase in BMI (kg/m2)
## 7 Estimates are per 1 kg/m2 increase in genetically predicted BMI
## 8 Estimates are per 1 kg/m2 increase in genetically predicted BMI
## 9 Effect per SD change of BMI on trait(SD)
## 10 effect per SD change of BMI on trait(SD)
## 11 effect per SD change of BMI on trait(SD)
## 12 effect per SD change of BMI on trait (SD)
## 13 effect per SD change in BMI on trait(SD scale)
## 14 effect per SD change of BMI on trait (SD scale)
## 15 effect per SD change of BMI(SD)
## 16
## 17 effect per SD change of BMI(SD)
## 18 effect per SD change of BMI on trait (SD scale)
## 19 IV estimate of SD change of SBP for a 1 SD change of log BMI
## 20 IV estimate of SD change of SBP for a 1 SD change of log BMI
## 21 IV estimate of SD change of SBP for a 1 SD change of log BMI
## 22 1 SD (4.8KG/M2) change in BMI , mm Hg (unstandardised beta coefficients)
## 23 1 SD (4.8kg/m2) change in BMI per change in mm Hg
## 24 Per 1 SD change in BMI (4.8kg/m2)
## 25 1 kg/m2 increase in BMI
## 26 1 Kg/m2 increase in BMI
## 27
## 28
## 29
## 30
## 31 1 SD increase in BMI allele score proportional to 0.64kg/m2 increase in BMI
## 32 Genetically predicted 1kg/m2 increase in body mass index
## 33 Genetically predicted 1kg/m2 increase in body mass index
## 34 Genetically predicted 1kg/m2 increase in fat mass index
## 35 Genetically predicted 1kg/m2 increase in body mass index
## 36 Genetically predicted 1kg/m2 increase in fat-free mass index
## 37 Genetically predicted 1kg/m2 increase in body mass index
## 38 ORs are given per 1 kg increase in visceral adipose tissue (VAT)
## 39 ORs are given per 1 kg increase in visceral adipose tissue (VAT)
## 40 ORs are given per 1 kg increase in visceral adipose tissue (VAT)
## 41 ORs are given per 1 kg increase in visceral adipose tissue (VAT)
## 42 percentage change in DBP due to a 1% change in BMI
## 43 1 SD increase in WHR (0.064)
## 44 1 SD increase in waist hip ratio(0.064)
## 45 1 SD increase in BMI (4.796kg/m2)
## 46 1 SD increase in BMI (4.796kg/m2)
## 47 Each unit increase in BMI increased high blood pressure by 1.13 percentage points
## 48 Each unit increase in BMI increased the risk of having high blood pressure by
## 49 Each unit increase in BMI increases high blood pressure by 1.42 percentage point
## 50 Each unit increase in BMI increased high blood pressure by 0.47 percentage point
## 51 Each unit increase in BMI increases high blood pressure by 1.26percentage point
## 52 Each unit increase in BMI increases high blood pressure by 1.38 percentage point
## 53 Each unit increase in BMI increased high blood pressure by 0.76 percentage points
## 54 Each unit increase in BMI increased high blood pressure by 0.83 percentage points
## 55 Each unit increase in BMI increases high blood pressure by 1.38 percentage point
## 56 Each unit increase in BMI increased high blood pressure by 1.04 percentage point
## 57 Each unit increase BMI increased the risk of having high blood pressure by 1.59 percentage points
## 58 Each unit increase in BMI increases high blood pressure by 0.92 percentage point
## 59
## 60 Per 1 SD increase in BMI
## 61 per 1 SD increase in BMI
## 62 per 1 SD increase in BMI
## 63 per 1 SD increase in BMI
## 64 per 1 SD increase in BMI
## 65 per 1 SD increase in BMI
## 66 per 1 SD increase in BMI
## 67 Per 1 SD increase in BMI
## 68 per 1 SD increase in BMI
## 69 per 1 SD increase in BMI
## 70 OR per 1 SD or 4.1kg/m2 of higher BMI
## 71 OR per 1 SD or 4.1kg/m2 of higher BMI
## 72 OR per 1 SD or 4.1kg/m2 of higher BMI
## 73 OR per 1 SD or 4.1 kg/m2 of higher BMI
## 74 OR per one SD or 4.1kg/m2 higher BMI
## 75 1 SD increase in childhood BMI is associated with 11% odds of essential hypertension
## 76 1 SD increase in childhood BMI associated with 14% odds for high blood pressure
## 77 1 SD increase in childhood BMI is associated with 13% odds for high blood pressure
## 78 1 SD increase in adult BMI is associated with 23% odds for essential hypertension
## 79 1 SD increase in childhood BMI is associated with 37% odds for high blood pressure
## 80 1 SD increase in adult BMI is associated with 21% odds for essential hypertension
## 81 1 SD increase in adult BMI is associated with 23% odds for essential hypertension
## 82 1 SD increase in adult BMI is associated with 30% odds for high blood pressure
## 83 1 SD increase in childhood BMI is associated with 21% odds of essential hypertension
## 84 1 SD increase in adult BMI is associated with 25% odds for high blood pressure
## 85 1 SD increase in adult BMI is associated with 30% odds for high blood pressure
## 86 1 SD increase in childhood BMI was associated with 12% higher odds of essential hypertension
## 87 1 SD increase in childhood BMI is associated with 14% odds for high blood pressure
## 88 1 SD increase in adult BMI is associated with 14% odds for essential hypertension
## 89 1 SD increase in adult BMI is associated with 31% odds of high blood pressure
## 90 1 SD increase in childhood BMI is associated with 11% odds of essential hypertension
## 91 1 SD higher UFA
## 92 1 SD higher genetically instrumented FA associated with decreased risk
## 93 A 1-SD higher genetically instrumented UFA (unfavourable adiposity) was associated with increased risk
## 94 1 SD genetically instrument FA
## 95 1 SD genetically instrumented FA
## 96 1 SD Genetically instrumented FA
## 97 1 SD higher genetically instrumented UFA
## 98 A 1 SD higher genetically instrumented UFA associated with increased risk in UKBB
## 99 1 SD higher genetically instrumented favourable adiposity (FA)
## 100 1 SD genetically instrumented
## 101 1 SD higher genetically instrumented FA was associated with lower risk
## 102 1 SD genetically instrumented UFA
## 103 Per 1 SD increase in childhood obesity
## 104 Per 1 SD increase in childhood obesity
## 105 per 1 SD increase in childhood body mass index
## 106 Per 1 SD increase in childhood obesity
## 107 Per 1 SD increase
## 108 Per 1 SD increase in VAT associated with 0.326 increase in SBP
## 109 Per 1 SD
## 110 Per 1 SD increase in GRS565 BMI
## 111 Per 1 SD
## 112 Per 1 SD
## 113 Per 1 SD
## 114 Per 1 SD increase
## 115 Per 1 SD
## 116 Per 1 SD
## 117 Per 1 SD increase in BF percentage
## 118 Per 1 SD
## 119 Per 1 SD
## 120 Per 1 SD
## 121 Per 1 SD
## 122 Per 1 SD
## 123 Per 1 SD
## 124 Per 1 SD
## 125 Per 1 SD
## 126 Per 1 SD
## 127 Per 1 SD
## 128 Per 1 SD
## 129 Per 1 SD
## 130 odds of each change in weight category
## 131 Per 1 SD change in gnetically determined BMI
## 132 Per 1 SD change in gnetically determined BMI
## 133 Per 1 SD change in gnetically determined BMI
## 134 Per 1 SD change in gnetically determined BMI
## 135
## 136 Per 1 SD increase in fat mass percentage(8.53)
## 137 per 1 SD increase in WHtR(0.07)
## 138 1 SD increase in BMI (4.93 kg/m2)
## 139 Per 1 SD increase in fat mass percentage (FMP)8.53
## 140 Per 1 SD increase in WHtR(0.07)
## 141 1 SD increase in BMI(4.83kg/m2)
## 142 1 SD increase in BMI
## 143
## 144
## 145
## 146
## 147
## notes
## 1 Log transformation of BMI to minimise skewness, 2 SNPs FTOrs9939609 and MC4Rrs17782313. To adjust for BP-lowering effect of medication 10 mm Hg was added to SBP
## 2 Log transformation of BMI to minimise skewness, used 2 SNPs(FTO and MCR4). Adjust for medication by 10mm Hg was added to DBP
## 3 Metaanalysis of 29 studies
## 4 Metaanalysis of 30 studies
## 5 Only one study, metaanalysis not performed.
## 6 Metaanalysis of 27 studies.
## 7 BMI allele score of 14 SNPs. A 1 kg/m2 increase in BMI increased SBP by 0.70 mmHg . 6 studies make up the population
## 8 Genetic allele risk score of 14 SNPs. A kg//m2 increase in BMI increase DBP by 0.28 mmHg. 6 studies
## 9 non weighted genetic risk score. nonstratified. 10mm Hg added to diastolic blood pressure where medication use was reported.
## 10 Stratified by age <55 years. Use of genetic allele score
## 11 Genetic allele score from 32 SNPs. Stratified by age greater than or equal to 55 years.
## 12 unweighted genetic risk score. 15 mmHg added to SBP. stratified on basis of less than 55 years.
## 13 Genetic allele score of 32 SNPs. greater or equal to 55 years.
## 14 unweighted allelic score of 32 SNPs . stratified by sex(women)
## 15 allelic risk score of 32 SNPs, stratified on basis of sex(women)
## 16 Non-weighted allelic score. 15mm Hg added to SBP where medication was reported.
## 17 unweighted allelic risk score of 32 SNPs and stratified on sex; men
## 18 unweighted allelic score of 32 SNPs, stratified on sex; men.
## 19 FTO SNP only used to compute for IV estimate
## 20 Test statistic is the mean difference (SD) per 1 SD greater log BMI age 8
## 21 IV estimated from 31 SNPs only excluding FTO
## 22 Polygenic risk score derived from 93 SNPs, adjusted for age, sex, 10 genetic PCs, alcohol intake, smoking, Townsend index and corrected for antihypertensive medication. Reported as unstandardised Beta coefficient
## 23 Polygenic risk score from the 93 SNPs, Beta estimates in mm Hg
## 24 The 1 SD represents 4.8kg/m2 change in BMI. Model adjusted for age, sex, smoking,alcohol intake, towns scores and 10 genetic PCs
## 25 count genetic risk scores, Korean population
## 26 weighted genetic risk score, Korean population
## 27 Allelic score computed from 96 variants and standardisation of BMI,SBP and allelic score.
## 28 Allele score computed from 96 SNPs, BMI, SBP and allelic score standardised using z-transformation.
## 29 Construct weighted allelic score using estimates from GIANT consortium. BMI, SBP and allelic score standardised using a z-score transformation
## 30 Allelic score from 96 SNPs, BMI, SBP and allelic score standardisation using z-transformation. UKBiobank cohort.
## 31 BMI is used as a surrogate of adiposity. Update MR method to MR pheWAS. 1 SD increase in BMI allele score is associated with a 0.64kg/m2 increase in BMI.
## 32 BMI as a surrogate for adiposity
## 33 BMI as a surrogate for adiposity
## 34 Fat mass index measured using bioelectrical impedance and calculated as analogous to BMI by dividing it with height squared
## 35 BMI as a surrogate for adiposity
## 36 Measured using bioelectric impedance and calculated analogous to BMI by dividing by height squared
## 37 BMI as a surrogate for adiposity. 11 outlier identified using MR-PRESSO
## 38 They predicted VAT from that measured in 4198 individuals using dual energy x-ray absorptiometry
## 39 They predicted VAT from that measured in 4198 individuals using dual energy x-ray absorptiometry.
## 40 They predicted VAT from that measured in 4198 individuals using dual energy x-ray absorptiometry
## 41 They predicted VAT from that measured in 4198 individuals using dual energy x-ray absorptiometry
## 42 log transformed the DBP and BMI due to skewness, 2SLS with zero invalid instruments
## 43 ALI and CPOOA, GRS
## 44 ALIR and CPOOA study, GRS
## 45 ALIR and CPOOA study, GRS
## 46 ALIR AND CPOOA studies in China, GRS
## 47 Average across HUNT and UKBiobank among siblings with family fixed effects, weighted polygenic risk scores
## 48 average across HUNT and UKBiobank, individual participant data and among siblings
## 49 Average estimates in HUNT ad UKBiobank, weighted PRS, 2SMR using weighted modal, non overlapping samples
## 50 Average estimate across HUNT and UKBiobank study, MR-Egger slope, non-overlapping samples among siblings
## 51 Effect estimates from 23andme study, non-overlapping samples among siblings. Replication of results from HUNT and UKBiobank on 23andme data
## 52 Effects estimates from 23andme study, non-overlapping sibling samples. Replication of results from HUNT and UKBiobank. Weigted median
## 53 Average across HUNT and UKBiobank among siblings, split sample meaning SNP-exposure and SNP-outcome was from different siblings; 2SMR.
## 54 Average across HUNT and UKBiobank, weighted PRS among siblings. using 2SMR SNP-Exposure and SNP-outcome from same sample
## 55 Effect estimates from 23andme, non-overlapping sibling samples and weighted mode, replication results of HUNT and UKBiobank
## 56 Effect estimates from 2SMR using weighted median, averaged from HUNT and UKBiobank study, non-overlapping samples
## 57 This entails an average estimate across HUNT and UK Biobank study. This analysis entails Individual participant data among the unrelated, using the weighted polygenic risk scores.
## 58 Effect estimates from 23andme, replication results of HUNT and UKBiobank study; non-overlapping sibling samples and MR-Egger slope
## 59 Vanderbilts biobank, GIANT study, strict GRS
## 60 Use of individual SNPs, the result represents the pooled estimates from the two cohorts using a fixed effect model.
## 61 Individual SNPs and results of FinnGen study using MR PRESSO
## 62 Individual SNPs and UKB study using MR-Egger-evidence for potential pleiotropy
## 63 Individual SNPs and UKB study using MR-PRESSO
## 64 Individual SNPs and results of UKBiobank using IVW
## 65 Individual SNPs used and results of FinnGen study using weighted median method
## 66 Individual SNPs and results of FinnGen study using MR-Egger
## 67 Use of individual SNPs and results are specifically for FinnGen study
## 68 Individual SNPs and UKB study with self reported hypertension
## 69 Individual SNPs and UKB study using weighted median
## 70 Genetic allele score from 76 variants
## 71 Genetic allele score of 59 variants
## 72 Genetic allele score of 76 variants
## 73 Genetic allele score of 76 variants
## 74 Genetic risk score calculated from 76 variants.
## 75
## 76 Vascular/heart problems diagnosed by doctor: High blood pressure
## 77 Vascular/heart problems diagnosed by doctor: High blood pressure
## 78
## 79 Vascular/heart problems diagnosed by doctor: high blood pressure
## 80
## 81
## 82 vascular/herat problems diagnosed by doctor:high blood pressure
## 83
## 84 Vascular/heart problems diagnosed by doctor:high blood pressure
## 85 Vascular/heart problems diagnosed by doctor: high blood pressure
## 86
## 87 Vascular/heart problems diagnosed by doctor
## 88
## 89 Vascular/heart problems diagnosed by doctor:high blood pressure
## 90
## 91 UKBB only, unfavourable adiposity
## 92 UKBB, favourable adiposity genetic risk scores
## 93 UKBB independent data sets including published GWAS and FinnGen. UFA genetic score associated with higher body fat percentage.
## 94 FinnGen study, FA
## 95 FinnGen study and FA
## 96 UKBB, FA favorable adiposity
## 97 UKBB only, unfavourable adiposity
## 98 UKBB, unfavourable adiposity (UFA) genetic risk scores taken from body fat percentage distribution
## 99 UKBB only, favourable adiposity
## 100 FinnGen study, unfavourable adiposity
## 101 Public GWAS and FinnGen independent of UKBB. Genetic risk scores of Favourable adiposity.
## 102 FinnGen study, Unfavorable adiposity
## 103 UKBiobank essential hypertension, includes SNPs that did not get to significance threshold
## 104 UKBioank essential hypertension consortium, GWAS reaching the pvalue significance threshold
## 105 UKBiobank essential hypertension consortium, no pleiotropy
## 106 UKBiobank essential hypertension consortium, included SNPs not meeting significance threshold
## 107 manual measurement of SBP at baseline in MDC cohort, GRS;UKB and GIANT
## 108 Manual measurement of SBP, GRS;UKB
## 109 SBP baseline in MDC Cohort, GRS ;UKBiobank
## 110 Manual measurement of SBP at baseline in MPP cohort, GRS score . UKB and GIANT for GRS weighting
## 111 DBP measured in MDC cohort at baseline. GRS;UKB and GIANT
## 112 manual measurement of SBP in MDC cohort at baseline,GRS; UKB, Body fat GRS approximated on BMI
## 113 Manual measurement of SBP, GRS, UKB
## 114 Manual measurement of SBP in MDC cohort, GRS;UKB and GIANT
## 115 SBP measured in MPP cohort at followup, GRS;UKB and GIANT
## 116 SBP measured in MPP at followup, GRS;UKB and GIANT
## 117 SBP measured in MPP at followup, GRS;UKB and GIANT
## 118 SBP measured in MPP at followup, GRS;UKB and GIANT, VAT GRS on BMI
## 119 Diastolic blood pressure in MPP cohort, baseline measures, GRS;UKB and GIANT
## 120 DBP in MPP cohort baseline measurement, GRS;UKB and GIANT.
## 121 DBP in MPP cohort at baseline, Body fat GRS on BMI, GRS;UKB
## 122 DBP measured in MPP cohort at baseline, GRS;UKB,VATgrs on BMI
## 123 DBP measured in MPP at followup, GRS;UKB, VAT GRS on BMI
## 124 DBP measured in MDC cohort, GRS;UKB
## 125 DBP measured in MDC cohort at baseline, GRS;UKB. BF GRS on BMI
## 126 DBP measured in MPP at followup, GRS; UKB and GIANT, BF GRS on BMI
## 127 DBP measured in MDC at baseline, GRS;UKB, VAT GRS on BMI
## 128 DBP measured in MPP at followup, GRS;UKB and GIANT
## 129 DBP measured in MPP cohort follow up, GRS;UKB and GIANT
## 130 UKBiobank cohort exposure variable, SNP-outcome from FinnGen study. Transformed the BMI into categorical data in UKBB.
## 131 Favourable adiposity (FA)
## 132 Finngen cohort, BMI as exposure
## 133 Unfavourable adiposity
## 134 Body fat percentage measured in UKBiobank
## 135 The instruments were summarised into a weighted polygenic risk score similar to what is Lyalls paper. The weights derived form the effect estimated reported by GIANT (beta per 1-SD unit of BMI)
## 136 BCAMS, fat mass percentage, Genetic risk score
## 137 BCAMS
## 138 Beijing Children and adolescents Metabolic Syndrome study
## 139 BCAMS,GRS
## 140 BCAMS, GRS
## 141 BCAMS, GRS
## 142 Vanderbilts biobank, no significant saps used SNPs at 10e-6
## 143 Hypertension disorders during pregnancy. Two cohorts Giant(exposure) and FinnGen(outcome).
## 144 Hypertension disorders during pregnancy. Two cohorts GIANT(exposure) and FinnGen(outcome)
## 145 hypertension disorders during pregnancy, two cohorts GIANT (exposure) and FinnGen (outcome).
## 146 Z-transformation to standardise BMI, SBP and weighted PRS. Construct a weighted PRS using the variants from Giant consortium
## 147 z-transformation to standardise BMI, SBP and weighted PRS. Construct a weighted PRS using the variants from Giant consortium. MR-GENIUS
## title
## 1 Does Greater Adiposity Increase Blood Pressure and Hypertension Risk? Mendelian Randomization Using the FTO/MC4R Genotype
## 2 Does Greater Adiposity Increase Blood Pressure and Hypertension Risk? Mendelian Randomization Using the FTO/MC4R Genotype
## 3 The Role of Adiposity in Cardiometabolic Traits: A Mendelian Randomization Analysis
## 4 The Role of Adiposity in Cardiometabolic Traits: A Mendelian Randomization Analysis
## 5 The Role of Adiposity in Cardiometabolic Traits: A Mendelian Randomization Analysis
## 6 The Role of Adiposity in Cardiometabolic Traits: A Mendelian Randomization Analysis
## 7 Causal Effects of Body Mass Index on Cardiometabolic Traits and Events: A Mendelian Randomization Analysis
## 8 Causal Effects of Body Mass Index on Cardiometabolic Traits and Events: A Mendelian Randomization Analysis
## 9 Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors
## 10 Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors
## 11 Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors
## 12 Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors
## 13 Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors
## 14 Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors
## 15 Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors
## 16 Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors
## 17 Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors
## 18 Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors
## 19 MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization
## 20 MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization
## 21 MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization
## 22 Association of Body Mass Index With Cardiometabolic Disease in the UK Biobank A Mendelian Randomization Study
## 23 Association of Body Mass Index With Cardiometabolic Disease in the UK Biobank A Mendelian Randomization Study
## 24 Association of Body Mass Index With Cardiometabolic Disease in the UK Biobank A Mendelian Randomization Study
## 25 Causal association of body mass index with hypertension using a Mendelian randomization design
## 26 Causal association of body mass index with hypertension using a Mendelian randomization design
## 27 Detecting and correcting for bias in Mendelian randomization analyses using Gene-by- Environment interactions
## 28 Detecting and correcting for bias in Mendelian randomization analyses using Gene-by- Environment interactions
## 29 Detecting and correcting for bias in Mendelian randomization analyses using Gene-by- Environment interactions
## 30 Detecting and correcting for bias in Mendelian randomization analyses using Gene-by- Environment interactions
## 31 Searching for the causal effects of body mass index in over 300 000 participants in UK Biobank, using Mendelian randomization
## 32 Bodymass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: aMendelian randomization study
## 33 Bodymass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: aMendelian randomization study
## 34 Bodymass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: aMendelian randomization study
## 35 Bodymass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: aMendelian randomization study
## 36 Bodymass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: aMendelian randomization study
## 37 Bodymass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: aMendelian randomization study
## 38 Contribution of genetics to visceral adiposity and its relation to cardiovascular and metabolic disease
## 39 Contribution of genetics to visceral adiposity and its relation to cardiovascular and metabolic disease
## 40 Contribution of genetics to visceral adiposity and its relation to cardiovascular and metabolic disease
## 41 Contribution of genetics to visceral adiposity and its relation to cardiovascular and metabolic disease
## 42 On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments
## 43 Causal associations of body mass index and waist-to-hip ratio with cardiometabolic traits among Chinese children: A Mendelian randomization study
## 44 Causal associations of body mass index and waist-to-hip ratio with cardiometabolic traits among Chinese children: A Mendelian randomization study
## 45 Causal associations of body mass index and waist-to-hip ratio with cardiometabolic traits among Chinese children: A Mendelian randomization study
## 46 Causal associations of body mass index and waist-to-hip ratio with cardiometabolic traits among Chinese children: A Mendelian randomization study
## 47 Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
## 48 Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
## 49 Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
## 50 Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
## 51 Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
## 52 Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
## 53 Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
## 54 Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
## 55 Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
## 56 Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
## 57 Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
## 58 Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
## 59 BMI Is Causally Associated With Pulmonary Artery Pressure But Not Hemodynamic Evidence of Pulmonary Vascular Remodeling
## 60 Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Hypertension A Mendelian Randomization Study
## 61 Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Hypertension A Mendelian Randomization Study
## 62 Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Hypertension A Mendelian Randomization Study
## 63 Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Hypertension A Mendelian Randomization Study
## 64 Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Hypertension A Mendelian Randomization Study
## 65 Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Hypertension A Mendelian Randomization Study
## 66 Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Hypertension A Mendelian Randomization Study
## 67 Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Hypertension A Mendelian Randomization Study
## 68 Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Hypertension A Mendelian Randomization Study
## 69 Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Hypertension A Mendelian Randomization Study
## 70 A data-driven approach for studying the role of body mass in multiple diseases: a phenome-wide registry-based case-control study in the UK Biobank
## 71 A data-driven approach for studying the role of body mass in multiple diseases: a phenome-wide registry-based case-control study in the UK Biobank
## 72 A data-driven approach for studying the role of body mass in multiple diseases: a phenome-wide registry-based case-control study in the UK Biobank
## 73 A data-driven approach for studying the role of body mass in multiple diseases: a phenome-wide registry-based case-control study in the UK Biobank
## 74 A data-driven approach for studying the role of body mass in multiple diseases: a phenome-wide registry-based case-control study in the UK Biobank
## 75 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 76 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 77 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 78 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 79 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 80 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 81 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 82 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 83 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 84 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 85 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 86 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 87 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 88 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 89 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 90 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 91 Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease
## 92 Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease
## 93 Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease
## 94 Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease
## 95 Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease
## 96 Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease
## 97 Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease
## 98 Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease
## 99 Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease
## 100 Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease
## 101 Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease
## 102 Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease
## 103 Birthweight, childhood obesity and risk of hypertension: aMendelian randomization study
## 104 Birthweight, childhood obesity and risk of hypertension: aMendelian randomization study
## 105 Birthweight, childhood obesity and risk of hypertension: aMendelian randomization study
## 106 Birthweight, childhood obesity and risk of hypertension: aMendelian randomization study
## 107 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 108 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 109 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 110 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 111 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 112 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 113 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 114 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 115 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 116 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 117 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 118 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 119 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 120 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 121 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 122 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 123 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 124 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 125 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 126 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 127 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 128 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 129 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 130 Mendelian Randomization Analyses Suggest Childhood Body Size Indirectly Influences End Points From Across the Cardiovascular Disease Spectrum Through Adult Body Size
## 131 Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation
## 132 Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation
## 133 Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation
## 134 Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation
## 135 Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy
## 136 Distinct causal effects of body fat distribution on cardiometabolic traits among children: Findings from the BCAMS study
## 137 Distinct causal effects of body fat distribution on cardiometabolic traits among children: Findings from the BCAMS study
## 138 Distinct causal effects of body fat distribution on cardiometabolic traits among children: Findings from the BCAMS study
## 139 Distinct causal effects of body fat distribution on cardiometabolic traits among children: Findings from the BCAMS study
## 140 Distinct causal effects of body fat distribution on cardiometabolic traits among children: Findings from the BCAMS study
## 141 Distinct causal effects of body fat distribution on cardiometabolic traits among children: Findings from the BCAMS study
## 142 Genetic Determinants of Body Mass Index and Fasting Glucose Are Mediators of Grade 1 Diastolic Dysfunction
## 143 Genetically Predicted Obesity Causally Increased the Risk of Hypertension Disorders in Pregnancy
## 144 Genetically Predicted Obesity Causally Increased the Risk of Hypertension Disorders in Pregnancy
## 145 Genetically Predicted Obesity Causally Increased the Risk of Hypertension Disorders in Pregnancy
## 146 Interaction-based Mendelian randomization with measured and unmeasured gene-bycovariate interactions
## 147 Interaction-based Mendelian randomization with measured and unmeasured gene-bycovariate interactions
## studyaim
## 1 To estimate the strength of association between body mass index/adiposity with blood pressure
## 2 To estimate the strength of association between body mass index/adiposity with blood pressure
## 3 Aimed to determine whether adiposity is causally related to various cardiometabolic traits using the Mendelian randomization approach.
## 4 Aimed to determine whether adiposity is causally related to various cardiometabolic traits using the Mendelian randomization approach.
## 5 Aimed to determine whether adiposity is causally related to various cardiometabolic traits using the Mendelian randomization approach.
## 6 Aimed to determine whether adiposity is causally related to various cardiometabolic traits using the Mendelian randomization approach.
## 7 To investigate the role of BMI in cardiometabolic traits and vents through IV analysis using MR approach.
## 8 To investigate the role of BMI in cardiometabolic traits and vents through IV analysis using MR approach.
## 9 To use MR design to assess whether adiposity causally affects known CVD risk factors at a similar magnitude in men and women and before and after 55 years.
## 10 To use MR design to assess whether adiposity causally affects known CVD risk factors at a similar magnitude in men and women and before and after 55 years.
## 11 To use MR design to assess whether adiposity causally affects known CVD risk factors at a similar magnitude in men and women and before and after 55 years.
## 12 To use MR design to assess whether adiposity causally affects known CVD risk factors at a similar magnitude in men and women and before and after 55 years.
## 13 To use MR design to assess whether adiposity causally affects known CVD risk factors at a similar magnitude in men and women and before and after 55 years.
## 14 To use MR design to assess whether adiposity causally affects known CVD risk factors at a similar magnitude in men and women and before and after 55 years.
## 15 To use MR design to assess whether adiposity causally affects known CVD risk factors at a similar magnitude in men and women and before and after 55 years.
## 16 To use MR design to assess whether adiposity causally affects known CVD risk factors at a similar magnitude in men and women and before and after 55 years.
## 17 To use MR design to assess whether adiposity causally affects known CVD risk factors at a similar magnitude in men and women and before and after 55 years.
## 18 To use MR design to assess whether adiposity causally affects known CVD risk factors at a similar magnitude in men and women and before and after 55 years.
## 19 IV analysis to estimate the causal effect of BMI os 172 phenotypes
## 20 IV analysis to estimate the causal effect of BMI os 172 phenotypes
## 21 IV analysis to estimate the causal effect of BMI os 172 phenotypes
## 22 To investigate the causal estimates of the association between BMI and cardiometabolic disease outcome and traits using Mendelian randomization
## 23 To investigate the causal estimates of the association between BMI and cardiometabolic disease outcome and traits using Mendelian randomization
## 24 To investigate the causal estimates of the association between BMI and cardiometabolic disease outcome and traits using Mendelian randomization
## 25 To assess the causal effect of obesity on hypertension
## 26 To assess the causal effect of obesity on hypertension
## 27 To detect and correct for bias using MR Gene by Environment method analogous to two sample MR
## 28 To detect and correct for bias using MR Gene by Environment method analogous to two sample MR
## 29 To detect and correct for bias using MR Gene by Environment method analogous to two sample MR
## 30 To detect and correct for bias using MR Gene by Environment method analogous to two sample MR
## 31 To perform MR-pheWAS to search for the casual effects of BMI in UKB using PHESANT open-source phenomenon scan tool
## 32 To use MR design to investigate the associations of BMI with 13 CVDs and arterial hypertension.
## 33 To use MR design to investigate the associations of BMI with 13 CVDs and arterial hypertension.
## 34 To use MR design to investigate the associations of BMI with 13 CVDs and arterial hypertension.
## 35 To use MR design to investigate the associations of BMI with 13 CVDs and arterial hypertension.
## 36 To use MR design to investigate the associations of BMI with 13 CVDs and arterial hypertension.
## 37 To use MR design to investigate the associations of BMI with 13 CVDs and arterial hypertension.
## 38 Understanding the role of genetics in visceral adipose tissue measured by imaging and its role in disease
## 39 Understanding the role of genetics in visceral adipose tissue measured by imaging and its role in disease
## 40 Understanding the role of genetics in visceral adipose tissue measured by imaging and its role in disease
## 41 Understanding the role of genetics in visceral adipose tissue measured by imaging and its role in disease
## 42 Applied example of lasso method (causal effect estimate in presence of invalid ivs) in assessing effect of BMI on DBP
## 43 To explore and compare the causal relationship between BMI and waist to hip ratio with cardiometabolic traits in East Asian population
## 44 To explore and compare the causal relationship between BMI and waist to hip ratio with cardiometabolic traits in East Asian population
## 45 To explore and compare the causal relationship between BMI and waist to hip ratio with cardiometabolic traits in East Asian population
## 46 To explore and compare the causal relationship between BMI and waist to hip ratio with cardiometabolic traits in East Asian population
## 47 To describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family -based analyses can reduce such biases.
## 48 To describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family -based analyses can reduce such biases.
## 49 To describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family -based analyses can reduce such biases.
## 50 To describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family -based analyses can reduce such biases.
## 51 To describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family -based analyses can reduce such biases.
## 52 To describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family -based analyses can reduce such biases.
## 53 To describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family -based analyses can reduce such biases.
## 54 To describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family -based analyses can reduce such biases.
## 55 To describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family -based analyses can reduce such biases.
## 56 To describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family -based analyses can reduce such biases.
## 57 To describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family -based analyses can reduce such biases.
## 58 To describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family -based analyses can reduce such biases.
## 59 Is BMI causally associated with pulmonary artery pressure or markers of pulmonary vascular remodelling?
## 60 To investigate the causal associations of 18 cardiovascular risk factors and lifestyle behaviours with hypertension risk using most recent largest GWAS
## 61 To investigate the causal associations of 18 cardiovascular risk factors and lifestyle behaviours with hypertension risk using most recent largest GWAS
## 62 To investigate the causal associations of 18 cardiovascular risk factors and lifestyle behaviours with hypertension risk using most recent largest GWAS
## 63 To investigate the causal associations of 18 cardiovascular risk factors and lifestyle behaviours with hypertension risk using most recent largest GWAS
## 64 To investigate the causal associations of 18 cardiovascular risk factors and lifestyle behaviours with hypertension risk using most recent largest GWAS
## 65 To investigate the causal associations of 18 cardiovascular risk factors and lifestyle behaviours with hypertension risk using most recent largest GWAS
## 66 To investigate the causal associations of 18 cardiovascular risk factors and lifestyle behaviours with hypertension risk using most recent largest GWAS
## 67 To investigate the causal associations of 18 cardiovascular risk factors and lifestyle behaviours with hypertension risk using most recent largest GWAS
## 68 To investigate the causal associations of 18 cardiovascular risk factors and lifestyle behaviours with hypertension risk using most recent largest GWAS
## 69 To investigate the causal associations of 18 cardiovascular risk factors and lifestyle behaviours with hypertension risk using most recent largest GWAS
## 70 to use phewas to investigate possible associations of high body mass index with multiple diseases.
## 71 to use phewas to investigate possible associations of high body mass index with multiple diseases.
## 72 to use phewas to investigate possible associations of high body mass index with multiple diseases.
## 73 to use phewas to investigate possible associations of high body mass index with multiple diseases.
## 74 to use phewas to investigate possible associations of high body mass index with multiple diseases.
## 75 To determine the effect of childhood BMI on adult traits
## 76 To determine the effect of childhood BMI on adult traits
## 77 To determine the effect of childhood BMI on adult traits
## 78 To determine the effect of childhood BMI on adult traits
## 79 To determine the effect of childhood BMI on adult traits
## 80 To determine the effect of childhood BMI on adult traits
## 81 To determine the effect of childhood BMI on adult traits
## 82 To determine the effect of childhood BMI on adult traits
## 83 To determine the effect of childhood BMI on adult traits
## 84 To determine the effect of childhood BMI on adult traits
## 85 To determine the effect of childhood BMI on adult traits
## 86 To determine the effect of childhood BMI on adult traits
## 87 To determine the effect of childhood BMI on adult traits
## 88 To determine the effect of childhood BMI on adult traits
## 89 To determine the effect of childhood BMI on adult traits
## 90 To determine the effect of childhood BMI on adult traits
## 91 To use MR to elucidate the potential causal role of favourable and unfavorable adiposity in metabolic syndrome.
## 92 To use MR to elucidate the potential causal role of favourable and unfavorable adiposity in metabolic syndrome.
## 93 To use MR to elucidate the potential causal role of favourable and unfavorable adiposity in metabolic syndrome.
## 94 To use MR to elucidate the potential causal role of favourable and unfavorable adiposity in metabolic syndrome.
## 95 To use MR to elucidate the potential causal role of favourable and unfavorable adiposity in metabolic syndrome.
## 96 To use MR to elucidate the potential causal role of favourable and unfavorable adiposity in metabolic syndrome.
## 97 To use MR to elucidate the potential causal role of favourable and unfavorable adiposity in metabolic syndrome.
## 98 To use MR to elucidate the potential causal role of favourable and unfavorable adiposity in metabolic syndrome.
## 99 To use MR to elucidate the potential causal role of favourable and unfavorable adiposity in metabolic syndrome.
## 100 To use MR to elucidate the potential causal role of favourable and unfavorable adiposity in metabolic syndrome.
## 101 To use MR to elucidate the potential causal role of favourable and unfavorable adiposity in metabolic syndrome.
## 102 To use MR to elucidate the potential causal role of favourable and unfavorable adiposity in metabolic syndrome.
## 103 To elucidate the causal relationship between childhood obesity and hypertension
## 104 To elucidate the causal relationship between childhood obesity and hypertension
## 105 To elucidate the causal relationship between childhood obesity and hypertension
## 106 To elucidate the causal relationship between childhood obesity and hypertension
## 107 To investigate the effect of modifying adiposity traits on blood pressure
## 108 To investigate the effect of modifying adiposity traits on blood pressure
## 109 To investigate the effect of modifying adiposity traits on blood pressure
## 110 To investigate the effect of modifying adiposity traits on blood pressure
## 111 To investigate the effect of modifying adiposity traits on blood pressure
## 112 To investigate the effect of modifying adiposity traits on blood pressure
## 113 To investigate the effect of modifying adiposity traits on blood pressure
## 114 To investigate the effect of modifying adiposity traits on blood pressure
## 115 To investigate the effect of modifying adiposity traits on blood pressure
## 116 To investigate the effect of modifying adiposity traits on blood pressure
## 117 To investigate the effect of modifying adiposity traits on blood pressure
## 118 To investigate the effect of modifying adiposity traits on blood pressure
## 119 To investigate the effect of modifying adiposity traits on blood pressure
## 120 To investigate the effect of modifying adiposity traits on blood pressure
## 121 To investigate the effect of modifying adiposity traits on blood pressure
## 122 To investigate the effect of modifying adiposity traits on blood pressure
## 123 To investigate the effect of modifying adiposity traits on blood pressure
## 124 To investigate the effect of modifying adiposity traits on blood pressure
## 125 To investigate the effect of modifying adiposity traits on blood pressure
## 126 To investigate the effect of modifying adiposity traits on blood pressure
## 127 To investigate the effect of modifying adiposity traits on blood pressure
## 128 To investigate the effect of modifying adiposity traits on blood pressure
## 129 To investigate the effect of modifying adiposity traits on blood pressure
## 130 To estimate the effect of childhood body size on 12 disease endpoints independently and after accounting for adult body size
## 131 Aimed to use MR and specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects on diseases
## 132 Aimed to use MR and specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects on diseases
## 133 Aimed to use MR and specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects on diseases
## 134 Aimed to use MR and specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects on diseases
## 135 To describe a suite of sensitivity analysis tools that enables investigators to quantify the robustness of their findings against such validity threats.
## 136 To explore and compare causal relationships of BMI, fat mass percentage and waist to height ratio with cardiometabolic traits in children.
## 137 To explore and compare causal relationships of BMI, fat mass percentage and waist to height ratio with cardiometabolic traits in children.
## 138 To explore and compare causal relationships of BMI, fat mass percentage and waist to height ratio with cardiometabolic traits in children.
## 139 To explore and compare causal relationships of BMI, fat mass percentage and waist to height ratio with cardiometabolic traits in children.
## 140 To explore and compare causal relationships of BMI, fat mass percentage and waist to height ratio with cardiometabolic traits in children.
## 141 To explore and compare causal relationships of BMI, fat mass percentage and waist to height ratio with cardiometabolic traits in children.
## 142 Delineating genetic drivers of modifiable risk factors for G1DD
## 143 To evaluate the causal association between obesity and hypertension disorders in pregnancy
## 144 To evaluate the causal association between obesity and hypertension disorders in pregnancy
## 145 To evaluate the causal association between obesity and hypertension disorders in pregnancy
## 146 MR GXE vs MR GENIUS to explore the effect of BMI on systolic blood pressure in UKBB
## 147 MR GXE vs MR GENIUS to explore the effect of BMI on systolic blood pressure in UKBB
## population sex mean_age median_age lower_age upper_age year samplesize
## 1 EUR both 0.0 0.00 0 0 2009 37027
## 2 EUR both 0.0 0.00 0 0 2009 37027
## 3 EUR both 0.0 0.00 0 0 2013 198502
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## 7 EUR both 60.0 0.00 17 100 2014 34538
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## 147 EUR both 0.0 0.00 0 0 2022 358928
## author
## 1 Nicholas J Timpson et al
## 2 Nicholas J Timpson et al
## 3 Tove Fall et al
## 4 Tove Fall et al
## 5 Tove Fall et al
## 6 Tove Fall et al
## 7 Michael V Holmes
## 8 Michael V Holmes
## 9 Tove Fall et al
## 10 Tove Fall et al
## 11 Tove Fall et al
## 12 Tove Fall et al
## 13 Tove Fall et al
## 14 Tove Fall et al
## 15 Tove Fall et al
## 16 Tove Fall et al
## 17 Tove Fall et al
## 18 Tove Fall et al
## 19 LouiseAC Millard et al
## 20 LouiseAC Millard et al
## 21 LouiseAC Millard et al
## 22 Donald M loyal et al
## 23 Donald M loyal et al
## 24 Donald M loyal et al
## 25 Mee-Ri Lee
## 26 Mee-Ri Lee
## 27 Wes Spiller et al
## 28 Wes Spiller et al
## 29 Wes Spiller et al
## 30 Wes Spiller et al
## 31 Louise A.C.Millard
## 32 Susanna C. Larsson et al
## 33 Susanna C. Larsson et al
## 34 Susanna C. Larsson et al
## 35 Susanna C. Larsson et al
## 36 Susanna C. Larsson et al
## 37 Susanna C. Larsson et al
## 38 Torgny Karlsson et al
## 39 Torgny Karlsson et al
## 40 Torgny Karlsson et al
## 41 Torgny Karlsson et al
## 42 Frank Windmeijer et al
## 43 Qiying Song
## 44 Qiying Song
## 45 Qiying Song
## 46 Qiying Song
## 47 Ben Brompton et al
## 48 Ben Brompton et al
## 49 Ben Brompton et al
## 50 Ben Brompton et al
## 51 Ben Brompton et al
## 52 Ben Brompton et al
## 53 Ben Brompton et al
## 54 Ben Brompton et al
## 55 Ben Brompton et al
## 56 Ben Brompton et al
## 57 Ben Brompton et al
## 58 Ben Brompton et al
## 59 Timothy E. Thayer
## 60 Van Oort Sabine et al
## 61 Van Oort Sabine et al
## 62 Van Oort Sabine et al
## 63 Van Oort Sabine et al
## 64 Van Oort Sabine et al
## 65 Van Oort Sabine et al
## 66 Van Oort Sabine et al
## 67 Van Oort Sabine et al
## 68 Van Oort Sabine et al
## 69 Van Oort Sabine et al
## 70 Elina Hypponen
## 71 Elina Hypponen
## 72 Elina Hypponen
## 73 Elina Hypponen
## 74 Elina Hypponen
## 75 Shan-Shan Dong et al
## 76 Shan-Shan Dong et al
## 77 Shan-Shan Dong et al
## 78 Shan-Shan Dong et al
## 79 Shan-Shan Dong et al
## 80 Shan-Shan Dong et al
## 81 Shan-Shan Dong et al
## 82 Shan-Shan Dong et al
## 83 Shan-Shan Dong et al
## 84 Shan-Shan Dong et al
## 85 Shan-Shan Dong et al
## 86 Shan-Shan Dong et al
## 87 Shan-Shan Dong et al
## 88 Shan-Shan Dong et al
## 89 Shan-Shan Dong et al
## 90 Shan-Shan Dong et al
## 91 Susan Martin et al
## 92 Susan Martin et al
## 93 Susan Martin et al
## 94 Susan Martin et al
## 95 Susan Martin et al
## 96 Susan Martin et al
## 97 Susan Martin et al
## 98 Susan Martin et al
## 99 Susan Martin et al
## 100 Susan Martin et al
## 101 Susan Martin et al
## 102 Susan Martin et al
## 103 Jingwen Fan
## 104 Jingwen Fan
## 105 Jingwen Fan
## 106 Jingwen Fan
## 107 Alice Giontella
## 108 Alice Giontella
## 109 Alice Giontella
## 110 Alice Giontella
## 111 Alice Giontella
## 112 Alice Giontella
## 113 Alice Giontella
## 114 Alice Giontella
## 115 Alice Giontella
## 116 Alice Giontella
## 117 Alice Giontella
## 118 Alice Giontella
## 119 Alice Giontella
## 120 Alice Giontella
## 121 Alice Giontella
## 122 Alice Giontella
## 123 Alice Giontella
## 124 Alice Giontella
## 125 Alice Giontella
## 126 Alice Giontella
## 127 Alice Giontella
## 128 Alice Giontella
## 129 Alice Giontella
## 130 Grace M. Power
## 131 Susan Martin et al
## 132 Susan Martin et al
## 133 Susan Martin et al
## 134 Susan Martin et al
## 135 Carlos Cinelli et al
## 136 Liwan Fu et al
## 137 Liwan Fu et al
## 138 Liwan Fu et al
## 139 Liwan Fu et al
## 140 Liwan Fu et al
## 141 Liwan Fu et al
## 142 Nataraja Sarma Vaitinadin et al
## 143 Wenting Wang et al
## 144 Wenting Wang et al
## 145 Wenting Wang et al
## 146 Wes Spiller et al
## 147 Wes Spiller et al
## UID.1
## 1 Nicholas J Timpson et al_19470880_2009_R57
## 2 Nicholas J Timpson et al_19470880_2009_R58
## 3 Tove Fall et al_23824655_2013_R3
## 4 Tove Fall et al_23824655_2013_R4
## 5 Tove Fall et al_23824655_2013_R2
## 6 Tove Fall et al_23824655_2013_R1
## 7 Michael V Holmes_24462370_2014_R92
## 8 Michael V Holmes_24462370_2014_R93
## 9 Tove Fall et al_25712996_2015_R77
## 10 Tove Fall et al_25712996_2015_R79
## 11 Tove Fall et al_25712996_2015_R80
## 12 Tove Fall et al_25712996_2015_R81
## 13 Tove Fall et al_25712996_2015_R82
## 14 Tove Fall et al_25712996_2015_R83
## 15 Tove Fall et al_25712996_2015_R84
## 16 Tove Fall et al_25712996_2015_R78
## 17 Tove Fall et al_25712996_2015_R86
## 18 Tove Fall et al_25712996_2015_R85
## 19 LouiseAC Millard et al_26568383_2015_R7
## 20 LouiseAC Millard et al_26568383_2015_R5
## 21 LouiseAC Millard et al_26568383_2015_R6
## 22 Donald M loyal et al_28678979_2017_R44
## 23 Donald M loyal et al_28678979_2017_R43
## 24 Donald M loyal et al_28678979_2017_R42
## 25 Mee-Ri Lee_30045251_2018_R108
## 26 Mee-Ri Lee_30045251_2018_R109
## 27 Wes Spiller et al_30462199_2018_R74
## 28 Wes Spiller et al_30462199_2018_R76
## 29 Wes Spiller et al_30462199_2018_R73
## 30 Wes Spiller et al_30462199_2018_R75
## 31 Louise A.C.Millard_30707692_2019_R59
## 32 Susanna C. Larsson et al_31195408_2020_R64
## 33 Susanna C. Larsson et al_31195408_2020_R65
## 34 Susanna C. Larsson et al_31195408_2020_R67
## 35 Susanna C. Larsson et al_31195408_2020_R63
## 36 Susanna C. Larsson et al_31195408_2020_R68
## 37 Susanna C. Larsson et al_31195408_2020_R66
## 38 Torgny Karlsson et al_31501611_2019_R10
## 39 Torgny Karlsson et al_31501611_2019_R8
## 40 Torgny Karlsson et al_31501611_2019_R11
## 41 Torgny Karlsson et al_31501611_2019_R9
## 42 Frank Windmeijer et al_31708716_2018_R41
## 43 Qiying Song_32636122_2020_R123
## 44 Qiying Song_32636122_2020_R121
## 45 Qiying Song_32636122_2020_R122
## 46 Qiying Song_32636122_2020_R120
## 47 Ben Brompton et al_32665587_2020_R15
## 48 Ben Brompton et al_32665587_2020_R14
## 49 Ben Brompton et al_32665587_2020_R19
## 50 Ben Brompton et al_32665587_2020_R20
## 51 Ben Brompton et al_32665587_2020_R21
## 52 Ben Brompton et al_32665587_2020_R22
## 53 Ben Brompton et al_32665587_2020_R17
## 54 Ben Brompton et al_32665587_2020_R16
## 55 Ben Brompton et al_32665587_2020_R23
## 56 Ben Brompton et al_32665587_2020_R18
## 57 Ben Brompton et al_32665587_2020_R13
## 58 Ben Brompton et al_32665587_2020_R24
## 59 Timothy E. Thayer_32712226_2021_R124
## 60 Van Oort Sabine et al_33131310_2020_R47
## 61 Van Oort Sabine et al_33131310_2020_R51
## 62 Van Oort Sabine et al_33131310_2020_R54
## 63 Van Oort Sabine et al_33131310_2020_R55
## 64 Van Oort Sabine et al_33131310_2020_R52
## 65 Van Oort Sabine et al_33131310_2020_R49
## 66 Van Oort Sabine et al_33131310_2020_R50
## 67 Van Oort Sabine et al_33131310_2020_R48
## 68 Van Oort Sabine et al_33131310_2020_R56
## 69 Van Oort Sabine et al_33131310_2020_R53
## 70 Elina Hypponen_33323262_2019_R88
## 71 Elina Hypponen_33323262_2019_R91
## 72 Elina Hypponen_33323262_2019_R89
## 73 Elina Hypponen_33323262_2019_R90
## 74 Elina Hypponen_33323262_2019_R87
## 75 Shan-Shan Dong et al_33771188_2021_R27
## 76 Shan-Shan Dong et al_33771188_2021_R29
## 77 Shan-Shan Dong et al_33771188_2021_R30
## 78 Shan-Shan Dong et al_33771188_2021_R35
## 79 Shan-Shan Dong et al_33771188_2021_R32
## 80 Shan-Shan Dong et al_33771188_2021_R33
## 81 Shan-Shan Dong et al_33771188_2021_R34
## 82 Shan-Shan Dong et al_33771188_2021_R39
## 83 Shan-Shan Dong et al_33771188_2021_R28
## 84 Shan-Shan Dong et al_33771188_2021_R40
## 85 Shan-Shan Dong et al_33771188_2021_R38
## 86 Shan-Shan Dong et al_33771188_2021_R25
## 87 Shan-Shan Dong et al_33771188_2021_R31
## 88 Shan-Shan Dong et al_33771188_2021_R36
## 89 Shan-Shan Dong et al_33771188_2021_R37
## 90 Shan-Shan Dong et al_33771188_2021_R26
## 91 Susan Martin et al_33980691_2021_R101
## 92 Susan Martin et al_33980691_2021_R99
## 93 Susan Martin et al_33980691_2021_R96
## 94 Susan Martin et al_33980691_2021_R106
## 95 Susan Martin et al_33980691_2021_R107
## 96 Susan Martin et al_33980691_2021_R103
## 97 Susan Martin et al_33980691_2021_R100
## 98 Susan Martin et al_33980691_2021_R98
## 99 Susan Martin et al_33980691_2021_R102
## 100 Susan Martin et al_33980691_2021_R105
## 101 Susan Martin et al_33980691_2021_R97
## 102 Susan Martin et al_33980691_2021_R104
## 103 Jingwen Fan_34001814_2021_R112
## 104 Jingwen Fan_34001814_2021_R111
## 105 Jingwen Fan_34001814_2021_R110
## 106 Jingwen Fan_34001814_2021_R113
## 107 Alice Giontella_34120448_2021_R130
## 108 Alice Giontella_34120448_2021_R128
## 109 Alice Giontella_34120448_2021_R132
## 110 Alice Giontella_34120448_2021_R125
## 111 Alice Giontella_34120448_2021_R138
## 112 Alice Giontella_34120448_2021_R131
## 113 Alice Giontella_34120448_2021_R127
## 114 Alice Giontella_34120448_2021_R129
## 115 Alice Giontella_34120448_2021_R141
## 116 Alice Giontella_34120448_2021_R142
## 117 Alice Giontella_34120448_2021_R143
## 118 Alice Giontella_34120448_2021_R144
## 119 Alice Giontella_34120448_2021_R133
## 120 Alice Giontella_34120448_2021_R134
## 121 Alice Giontella_34120448_2021_R135
## 122 Alice Giontella_34120448_2021_R136
## 123 Alice Giontella_34120448_2021_R148
## 124 Alice Giontella_34120448_2021_R137
## 125 Alice Giontella_34120448_2021_R139
## 126 Alice Giontella_34120448_2021_R147
## 127 Alice Giontella_34120448_2021_R140
## 128 Alice Giontella_34120448_2021_R146
## 129 Alice Giontella_34120448_2021_R145
## 130 Grace M. Power_34465205_2021_R95
## 131 Susan Martin et al_35074047_2022_R71
## 132 Susan Martin et al_35074047_2022_R69
## 133 Susan Martin et al_35074047_2022_R72
## 134 Susan Martin et al_35074047_2022_R70
## 135 Carlos Cinelli et al_35232963_2022_R12
## 136 Liwan Fu et al_35599089_2022_R116
## 137 Liwan Fu et al_35599089_2022_R115
## 138 Liwan Fu et al_35599089_2022_R114
## 139 Liwan Fu et al_35599089_2022_R119
## 140 Liwan Fu et al_35599089_2022_R118
## 141 Liwan Fu et al_35599089_2022_R117
## 142 Nataraja Sarma Vaitinadin et al_35656995_2022_R94
## 143 Wenting Wang et al_35694671_2022_R62
## 144 Wenting Wang et al_35694671_2022_R61
## 145 Wenting Wang et al_35694671_2022_R60
## 146 Wes Spiller et al_35947639_2022_R45
## 147 Wes Spiller et al_35947639_2022_R46
## annotation
## 1 Measured using automatic digital blood pressure monitor moderated elevated BP(SBP > 140mm Hg),Adult,Systolic blood pressure,weight divided by height in square metres,Main,SBP estimated increase in mm Hg for each 10% increase in BMI,Log transformation of BMI to minimise skewness, 2 SNPs FTOrs9939609 and MC4Rrs17782313. To adjust for BP-lowering effect of medication 10 mm Hg was added to SBP
## 2 Measured using automatic digital blood pressure monitor elevated DBP >90mm Hg,Adult,Diastolic blood pressure,weight divided by height in square metres,sensitivity,DBP estimated increase in mm Hg for each 10% increase in BMI,Log transformation of BMI to minimise skewness, used 2 SNPs(FTO and MCR4). Adjust for medication by 10mm Hg was added to DBP
## 3 Self reporting, biochemical measurement, health registry and medical records,Adult,diastolic blood pressure in mm Hg,weight divided by height in square metres,Main,one unit increase in BMI (kg/m2),Metaanalysis of 29 studies
## 4 self reported, biochemical measurement, health registry and medical records,Adult,systolic blood pressure in mm Hg,weight divided by height in square metres,Main,one unit increase in BMI (kg/m2),Metaanalysis of 30 studies
## 5 self reported, biochemical measurement, health registry and medical records,Adult,Incident hypertension,weight divided by height in square metres,Main,one unit increase in BMI (kg/m2),Only one study, metaanalysis not performed.
## 6 Self reported, biochemical measurement, health registry and medical records,Adult,Ever hypertension,weight divided by height in square metres,Main,one unit increase in BMI (kg/m2),Metaanalysis of 27 studies.
## 7 ,Adult,Systolic blood pressure,weight divided by height in square metres,Main,Estimates are per 1 kg/m2 increase in BMI,BMI allele score of 14 SNPs. A 1 kg/m2 increase in BMI increased SBP by 0.70 mmHg . 6 studies make up the population
## 8 ,Adult,Diastolic blood pressure,weight divided by height in square metres,Main,estimates are per 1kg/m2 increase in BMI,genetic allele risk score of 14 SNPs. A kg//m2 increase in BMI increase DBP by 0.28 mmHg. 6 studies
## 9 ,Adult,Diastolic blood pressure,weight divided by height in square metres,Main,Effect per SD change of BMI on trait(SD),non weighted genetic risk score. nonstratified. 10mm Hg added to diastolic blood pressure where medication use was reported.
## 10 ,Adult,Diastolic blood pressure,weight divided by height in square metres,secondary,effect per SD change of BMI on trait(SD),Stratified by age <55 years. Use of genetic allele score
## 11 ,Adult,Diastolic blood pressure,weight divided by height in square metres,secondary,effect per SD change of BMI on trait(SD),Genetic allele score from 32 SNPs. Stratified by age greater than or equal to 55 years.
## 12 ,Adult,Systolic blood pressure,weight divided by height in square metres,secondary,effect per SD change of BMI on trait (SD),unweighted genetic risk score. 15 mmHg added to SBP. stratified on basis of less than 55 years.
## 13 ,Adult,Systolic blood pressure,weight divided by height in square metres,secondary,effect per SD change in BMI on trait(SD scale),Genetic allele score of 32 SNPs. greater or equal to 55 years.
## 14 ,Adult,Diastolic blood pressure,weight divided by height in square metres,secondary,effect per SD change of BMI on trait (SD scale),unweighted allelic score of 32 SNPs . stratified by sex(women)
## 15 ,Adult,Systolic blood pressure,weight divided by height in square metres,secondary,effect per SD change of BMI(SD),allelic risk score of 32 SNPs, stratified on basis of sex(women)
## 16 ,Adult,Systolic blood pressure,weight divided by height in square metres,Main,,Non-weighted allelic score. 15mm Hg added to SBP where medication was reported.
## 17 ,Adult,Systolic blood pressure,weight divided by height in square metres,secondary,effect per SD change of BMI(SD),unweighted allelic risk score of 32 SNPs and stratified on sex; men
## 18 ,Adult,Diastolic blood pressure,weight divided by height in square metres,secondary,effect per SD change of BMI on trait (SD scale),unweighted allelic score of 32 SNPs, stratified on sex; men.
## 19 ALSPAC (mm Hg),Childhood,systolic blood pressure not log transformed,weight divided by height in square metres (evaluated as log BMI),sensitivity,IV estimate of SD change of SBP for a 1 SD change of log BMI,FTO SNP only used to compute for IV estimate
## 20 ALSPAC (mm Hg),Childhood,systolic blood pressure not log transformed,weight divided by height in square metres,Main,IV estimate of SD change of SBP for a 1 SD change of log BMI ,Test statistic is the mean difference (SD) per 1 SD greater log BMI age 8
## 21 ALSPAC (mm Hg) ,Childhood,systolic blood pressure not log transformed,weight divided by height in square metres (evaluated as log BMI),sensitivity,IV estimate of SD change of SBP for a 1 SD change of log BMI,IV estimated from 31 SNPs only excluding FTO
## 22 digital blood pressure monitors,Adult,Diastolic blood pressure,weight divided by height in square metres,secondary,1 SD (4.8KG/M2) change in BMI , mm Hg (unstandardised beta coefficients),Polygenic risk score derived from 93 SNPs, adjusted for age, sex, 10 genetic PCs, alcohol intake, smoking, Townsend index and corrected for antihypertensive medication. Reported as unstandardised Beta coefficient
## 23 Using digital blood pressure monitor,Adult,Systolic blood pressure,weight divided by height in square metres,secondary,1 SD (4.8kg/m2) change in BMI per change in mm Hg,Polygenic risk score from the 93 SNPs, Beta estimates in mm Hg
## 24 Self reporting use of antihypertensive medication and having received doctor diagnosis,Adult,Hypertension,weight divided by height in square metres,Main,Per 1 SD change in BMI (4.8kg/m2),The 1 SD represents 4.8kg/m2 change in BMI. Model adjusted for age, sex, smoking,alcohol intake, towns scores and 10 genetic PCs
## 25 BP measured using mercury sphygmanometers (hypertensive SBP>140 or >90mm Hg for DBP) using antihypertensive within the 10 year followup,Adult,Hypertension,,Main,1 kg/m2 increase in BMI ,count genetic risk scores, Korean population
## 26 systolic /diastolic greater than 140mm Hg and 90 mm Hg respectively and use of medication,Adult,Hypertension,weight divided vbybheight in square metres,Main,1 Kg/m2 increase in BMI,weighted genetic risk score, Korean population
## 27 ,Adult,Systolic blood pressure,weight divided by height in square metres,sensitivity,,Allelic score computed from 96 variants and standardisation of BMI,SBP and allelic score.
## 28 ,Adult,Systolic blood pressure,weight divided by height in square metres,sensitivity,,Allele score computed from 96 SNPs, BMI, SBP and allelic score standardised using z-transformation.
## 29 ,Adult,Systolic blood pressure,weight divided by height in square metres,Main,,Construct weighted allelic score using estimates from GIANT consortium. BMI, SBP and allelic score standardised using a z-score transformation
## 30 ,Adult,Systolic blood pressure,weight divided by height in square metres,sensitivity,,Allelic score from 96 SNPs, BMI, SBP and allelic score standardisation using z-transformation. UKBiobank cohort.
## 31 hypertension as defined in UKBiobank,Adult,Hypertension,weight divided by height in square metres,Main,1 SD increase in BMI allele score proportional to 0.64kg/m2 increase in BMI,BMI is used as a surrogate of adiposity. Update MR method to MR pheWAS. 1 SD increase in BMI allele score is associated with a 0.64kg/m2 increase in BMI.
## 32 ICD 9 and 10, self reporting(doctors diagnosis,Adult,Arterial hypertension,weight divided by height in square metres,sensitivity,Genetically predicted 1kg/m2 increase in body mass index,BMI as a surrogate for adiposity
## 33 ICD 9 and 10, self diagnosis(doctor diagnosis),Adult,Arterial hypertension,weight divided by height in square metres,sensitivity,Genetically predicted 1kg/m2 increase in body mass index,BMI as a surrogate for adiposity
## 34 ICD 9 and 10, self reporting(doctor diagnosis),Adult,Arterial hypertension,Assessed using bioelectrical impedance technique. Fat mass index divided by height squared.,secondary,Genetically predicted 1kg/m2 increase in fat mass index,Fat mass index measured using bioelectrical impedance and calculated as analogous to BMI by dividing it with height squared
## 35 arterial hypertension aș assessed in UKBiobank,Adult,Arterial hypertension,weight divided by height in square metres,Main,Genetically predicted 1kg/m2 increase in body mass index,BMI as a surrogate for adiposity
## 36 ICD 9 and 10, self reporting (Doctor diagnosis),Adult,Arterial hypertension,assessed using bioelectric impedance,secondary,Genetically predicted 1kg/m2 increase in fat-free mass index,Measured using bioelectric impedance and calculated analogous to BMI by dividing by height squared
## 37 ICD 9 and 10, self diagnosis(doctor diagnosis),Adult,Arterial hypertension,weight divided by height in square metres,sensitivity,Genetically predicted 1kg/m2 increase in body mass index,BMI as a surrogate for adiposity. 11 outlier identified using MR-PRESSO
## 38 ,Adult,Hypertension,Measured using dual energy X-ray absorptiometry,secondary,ORs are given per 1 kg increase in visceral adipose tissue (VAT),They predicted VAT from that measured in 4198 individuals using dual energy x-ray absorptiometry
## 39 ,Adult,Hypertension,Measured using dual energy X-ray absorptiometry,Main,ORs are given per 1 kg increase in visceral adipose tissue (VAT),They predicted VAT from that measured in 4198 individuals using dual energy x-ray absorptiometry.
## 40 ,Adult,Hypertension,Measured using dual energy X-ray absorptiometry,secondary,ORs are given per 1 kg increase in visceral adipose tissue (VAT),They predicted VAT from that measured in 4198 individuals using dual energy x-ray absorptiometry
## 41 ,Adult,hypertension,Measured using dual energy X-ray absorptiometry,Main,ORs are given per 1 kg increase in visceral adipose tissue (VAT),They predicted VAT from that measured in 4198 individuals using dual energy x-ray absorptiometry
## 42 ,Adult,Diastolic blood pressure,weight divided by height I square metres,Main,percentage change in DBP due to a 1% change in BMI,log transformed the DBP and BMI due to skewness, 2SLS with zero invalid instruments
## 43 ,Childhood,Diastolic blood pressure,waist circumference divided by hip circumference,Main,1 SD increase in WHR (0.064),ALI and CPOOA, GRS
## 44 ,Childhood,Systolic blood pressure,waist circumference divided by hip circumference,Main, 1 SD increase in waist hip ratio(0.064),ALIR and CPOOA study, GRS
## 45 ,Childhood,Diastolic blood pressure,weight divided by height in square metres,Main,1 SD increase in BMI (4.796kg/m2),ALIR and CPOOA study, GRS
## 46 ,Childhood,Systolic blood pressure,weight divided by height in square metres,Main,1 SD increase in BMI (4.796kg/m2),ALIR AND CPOOA studies in China, GRS
## 47 Self reported high blood pressure with current antihypertensive medication and SBP/DBP greater than 140/90 mm Hg.,Adult,High blood pressure,weight divided by height in square metres,secondary,Each unit increase in BMI increased high blood pressure by 1.13 percentage points,Average across HUNT and UKBiobank among siblings with family fixed effects, weighted polygenic risk scores
## 48 self reported high blood pressure defined as either currently taking medication or SBP /DBP greater than 140/90 mm Hg respectively,Adult,High blood pressure,weight divided by height in square metres,Main,Each unit increase in BMI increased the risk of having high blood pressure by ,average across HUNT and UKBiobank, individual participant data and among siblings
## 49 Self reporting of high blood pressure , current use of medication or SBP/DBP greater than 140/90 mm Hg respectively,Adult,High blood pressure,weight divided by height I metres squared,sensitivity,Each unit increase in BMI increases high blood pressure by 1.42 percentage point,Average estimates in HUNT ad UKBiobank, weighted PRS, 2SMR using weighted modal, non overlapping samples
## 50 Self reporting of high blood pressure; current medication or SBP/DBP greater than 140/90 mm Hg,Adult,High blood pressure,weight divided by height in metres squared,sensitivity,Each unit increase in BMI increased high blood pressure by 0.47 percentage point,Average estimate across HUNT and UKBiobank study, MR-Egger slope, non-overlapping samples among siblings
## 51 Self reporting of high blood pressure with medication use or SBP/DBP greater than 140/90 mm Hg,Adult,High blood pressure,weight divided by height in square metres,secondary,Each unit increase in BMI increases high blood pressure by 1.26percentage point,Effect estimates from 23andme study, non-overlapping samples among siblings. Replication of results from HUNT and UKBiobank on 23andme data
## 52 self reporting of high blood pressure,Adult,High blood pressure,weight divided by height in square metres,sensitivity,Each unit increase in BMI increases high blood pressure by 1.38 percentage point,Effects estimates from 23andme study, non-overlapping sibling samples. Replication of results from HUNT and UKBiobank. Weigted median
## 53 self reported high blood pressure , current antihypertensive medication or SBP/DBP greater than 140/90 mm Hg,Adult,High blood pressure,weight divided by height in metres squared,secondary,Each unit increase in BMI increased high blood pressure by 0.76 percentage points,Average across HUNT and UKBiobank among siblings, split sample meaning SNP-exposure and SNP-outcome was from different siblings; 2SMR.
## 54 Self reported with current use of medication or SBP/DBP greater than 140/90 mm Hg,Adult,High blood pressure,weight divided by height in square metres,secondary,Each unit increase in BMI increased high blood pressure by 0.83 percentage points,Average across HUNT and UKBiobank, weighted PRS among siblings. using 2SMR SNP-Exposure and SNP-outcome from same sample
## 55 Self reporting of high blood pressure,Adult,High blood pressure,weight divided by height in square metres,sensitivity,Each unit increase in BMI increases high blood pressure by 1.38 percentage point,Effect estimates from 23andme, non-overlapping sibling samples and weighted mode, replication results of HUNT and UKBiobank
## 56 self reporting defined as current use of antihypertensive medication or SBP/DBP greater than 140/90 mm Hg respectively,Adult,High blood pressure,weight divided by height in square metres,sensitivity,Each unit increase in BMI increased high blood pressure by 1.04 percentage point,Effect estimates from 2SMR using weighted median, averaged from HUNT and UKBiobank study, non-overlapping samples
## 57 Self reported high blood pressure defined as either currently taking anti-hypertensive medication ad having systolic or diastolic blood pressure above 140mm Hg or 90 mm Hg respectively.,Adult,High blood pressure,weight divided by height in square metres,Main,Each unit increase BMI increased the risk of having high blood pressure by 1.59 percentage points,This entails an average estimate across HUNT and UK Biobank study. This analysis entails Individual participant data among the unrelated, using the weighted polygenic risk scores.
## 58 Self reporting of high blood pressure,Adult,High blood pressure,weight divided by height in square metres,sensitivity,Each unit increase in BMI increases high blood pressure by 0.92 percentage point,Effect estimates from 23andme, replication results of HUNT and UKBiobank study; non-overlapping sibling samples and MR-Egger slope
## 59 ,Adult,Pulmonary arterial hypertension,,Main,,Vanderbilts biobank, GIANT study, strict GRS
## 60 ICD 10 from discharge registries,Adult,Essential(primary) hypertension,weight divided by height in square metres,Main,Per 1 SD increase in BMI,Use of individual SNPs, the result represents the pooled estimates from the two cohorts using a fixed effect model.
## 61 I10 diagnosis in discharge registries,Adult,Essential(primary)hypertension,weight divided by height in square metres,sensitivity,per 1 SD increase in BMI,Individual SNPs and results of FinnGen study using MR PRESSO
## 62 I10 diagnosis of essential hypertension,Adult,Essential(primary)hypertension,weight divided by height in square metres,sensitivity,per 1 SD increase in BMI,Individual SNPs and UKB study using MR-Egger-evidence for potential pleiotropy
## 63 I10 diagnosis of essential hypertension,Adult,Essential(primary)hypertension,weight divided by height in square metres,sensitivity,per 1 SD increase in BMI,Individual SNPs and UKB study using MR-PRESSO
## 64 I10 diagnosis for essential hypertension,Adult,essential(primary)hypertension,weight divided by height in square metres,sensitivity,per 1 SD increase in BMI,Individual SNPs and results of UKBiobank using IVW
## 65 I10 diagnosis , discharge registries,Adult,Essential(primary) hypertension,weight divided by height in square metres,sensitivity,per 1 SD increase in BMI,Individual SNPs used and results of FinnGen study using weighted median method
## 66 I10 diagnosis in discharge registries,Adult,Essential(primary) hypertension,weight divided by height in square metres,sensitivity,per 1 SD increase in BMI,Individual SNPs and results of FinnGen study using MR-Egger
## 67 I10 diagnosis in discharge registries,Adult,Essential(primary) hypertension,weight divided by height in square metres,sensitivity,Per 1 SD increase in BMI,Use of individual SNPs and results are specifically for FinnGen study
## 68 self reported hypertension,Adult,Hypertension,weight divided by height in square metres,sensitivity,per 1 SD increase in BMI,Individual SNPs and UKB study with self reported hypertension
## 69 I10 diagnosis of essential hypertension,Adult,Essential(primary)hypertension,weight divided by height in square metres,sensitivity,per 1 SD increase in BMI,Individual SNPs and UKB study using weighted median
## 70 ,Adult,,weight divided by height in square metres,sensitivity,OR per 1 SD or 4.1kg/m2 of higher BMI,Genetic allele score from 76 variants
## 71 ,Adult,Hypertension,weight divided by height in square metres,sensitivity,OR per 1 SD or 4.1kg/m2 of higher BMI,Genetic allele score of 59 variants
## 72 ,Adult,Hypertension,weight divided by height in square metres,sensitivity,OR per 1 SD or 4.1kg/m2 of higher BMI,Genetic allele score of 76 variants
## 73 ,Adult,Hypertension,weight divided by height in square metres,sensitivity,OR per 1 SD or 4.1 kg/m2 of higher BMI,Genetic allele score of 76 variants
## 74 ,Adult,Hypertension,weight divided by height in square metres,Main,OR per one SD or 4.1kg/m2 higher BMI,Genetic risk score calculated from 76 variants.
## 75 I10 Essential hypertension,Childhood,Essential hypertension,weight divided by height in square metres,sensitivity,1 SD increase in childhood BMI is associated with 11% odds of essential hypertension,
## 76 Vascular/heart problems diagnosed by doctor: High blood pressure,Childhood,High blood pressure,weight divided by heigh in square metres,secondary,1 SD increase in childhood BMI associated with 14% odds for high blood pressure,Vascular/heart problems diagnosed by doctor: High blood pressure
## 77 Vascular/heart problems diagnosed by doctor: High blood pressure,Childhood,High blood pressure,weight divided by height in square metres,secondary,1 SD increase in childhood BMI is associated with 13% odds for high blood pressure,Vascular/heart problems diagnosed by doctor: High blood pressure
## 78 ,Adult,I10 Essential hypertension,weight divided by height in square metres,sensitivity,1 SD increase in adult BMI is associated with 23% odds for essential hypertension,
## 79 Vascular/heart problems diagnosed by doctor: high blood pressure,Childhood,High blood pressure,weight divided by height in square metres,secondary,1 SD increase in childhood BMI is associated with 37% odds for high blood pressure,Vascular/heart problems diagnosed by doctor: high blood pressure
## 80 ,Adult,I10 Essential hypertension,weight divided by height in square metres,Main,1 SD increase in adult BMI is associated with 21% odds for essential hypertension,
## 81 ,Adult,I10 Essential hypertension,weight divided by height in square metres,sensitivity,1 SD increase in adult BMI is associated with 23% odds for essential hypertension,
## 82 Vascular/heart problems diagnosed by doctor:high blood pressure,Adult,High blood pressure,weight divided by height in square metres,sensitivity,1 SD increase in adult BMI is associated with 30% odds for high blood pressure,vascular/herat problems diagnosed by doctor:high blood pressure
## 83 I10 Essential hypertension,Childhood,Essential hypertension,weight divided by height in square metres,sensitivity,1 SD increase in childhood BMI is associated with 21% odds of essential hypertension,
## 84 Vascular/heart problems diagnosed by doctor:high blood pressure,Adult,High blood pressure,weight divided by height in square metres,sensitivity,1 SD increase in adult BMI is associated with 25% odds for high blood pressure,Vascular/heart problems diagnosed by doctor:high blood pressure
## 85 Vascular/heart problems diagnosed by doctor: high blood pressure,Adult,High blood pressure,weight divided by height in square metres,sensitivity,1 SD increase in adult BMI is associated with 30% odds for high blood pressure,Vascular/heart problems diagnosed by doctor: high blood pressure
## 86 I10 Essential primary hypertension,Childhood,Essential hypertension,Weight divided by heigh in square metres,Main,1 SD increase in childhood BMI was associated with 12% higher odds of essential hypertension,
## 87 Vascular/heart problems diagnosed by doctor:high blood pressure,Childhood,High blood pressure,weight divided by height in square metres,secondary,1 SD increase in childhood BMI is associated with 14% odds for high blood pressure,Vascular/heart problems diagnosed by doctor
## 88 ,Adult,I10 essential hypertension,weight divided by height in square metres,sensitivity,1 SD increase in adult BMI is associated with 14% odds for essential hypertension,
## 89 Vascular/heart problems diagnosed by doctor:high blood pressure,Adult,High blood pressure,weight divided by height I square metres,Main,1 SD increase in adult BMI is associated with 31% odds of high blood pressure,Vascular/heart problems diagnosed by doctor:high blood pressure
## 90 I10 Essential hypertension,Childhood,Essential hypertension,weight divided by height in square metres,sensitivity,1 SD increase in childhood BMI is associated with 11% odds of essential hypertension,
## 91 ,Adult,Hypertension,MRI scan on body fat percentage,sensitivity,1 SD higher UFA,UKBB only, unfavourable adiposity
## 92 ,Adult,Hypertension,MRI scan of body fat percentage-favourable adiposity,Main,1 SD higher genetically instrumented FA associated with decreased risk,UKBB, favourable adiposity genetic risk scores
## 93 ,Adult,Hypertension,MRI scan to measure body fat percentage,Main,A 1-SD higher genetically instrumented UFA (unfavourable adiposity) was associated with increased risk,UKBB independent data sets including published GWAS and FinnGen. UFA genetic score associated with higher body fat percentage.
## 94 ,Adult,Hypertension,MRI scan of body fat percentage,sensitivity,1 SD genetically instrument FA,FinnGen study, FA
## 95 ,Adult,Hypertension,MRI scan of body fat percentage-FA,sensitivity,1 SD genetically instrumented FA,FinnGen study and FA
## 96 ,Adult,Hypertension,MRI scan of body fat percentage,sensitivity, 1 SD Genetically instrumented FA,UKBB, FA favorable adiposity
## 97 ,Adult,Hypertension,MRI scan of body fat percentage,sensitivity,1 SD higher genetically instrumented UFA,UKBB only, unfavourable adiposity
## 98 ,Adult,Hypertension,MRI scan for body fat percentage,Main,A 1 SD higher genetically instrumented UFA associated with increased risk in UKBB,UKBB, unfavourable adiposity (UFA) genetic risk scores taken from body fat percentage distribution
## 99 ,Adult,Hypertension,MRI scan for body fat percentage,sensitivity,1 SD higher genetically instrumented favourable adiposity (FA),UKBB only, favourable adiposity
## 100 ,Adult,Hypertension,MRI scan of body fat percentage,sensitivity,1 SD genetically instrumented,FinnGen study, unfavourable adiposity
## 101 ,Adult,Hypertension,MRI scan of body fat percentage,Main,1 SD higher genetically instrumented FA was associated with lower risk ,Public GWAS and FinnGen independent of UKBB. Genetic risk scores of Favourable adiposity.
## 102 ,Adult,Hypertension,MRI scan of body fat percentage,sensitivity,1 SD genetically instrumented UFA,FinnGen study, Unfavorable adiposity
## 103 ,Childhood,Hypertension,weight divided by height in square metres,Main,Per 1 SD increase in childhood obesity,UKBiobank essential hypertension, includes SNPs that did not get to significance threshold
## 104 ,Childhood,Hypertension,weight divided by height in square metres,sensitivity,Per 1 SD increase in childhood obesity,UKBioank essential hypertension consortium, GWAS reaching the pvalue significance threshold
## 105 ,Childhood,hypertension,weight divided by height in square metres,Main,per 1 SD increase in childhood body mass index,UKBiobank essential hypertension consortium, no pleiotropy
## 106 ,Childhood,Hypertension,weight divided by height in square metres,sensitivity,Per 1 SD increase in childhood obesity,UKBiobank essential hypertension consortium, included SNPs not meeting significance threshold
## 107 ,Adult,Systolic blood pressure,waistband hip circumference,Main,Per 1 SD increase,manual measurement of SBP at baseline in MDC cohort, GRS;UKB and GIANT
## 108 ,Adult,Systolic blood pressure,MRI scan of VAT from original GWAS (UKB),Main,Per 1 SD increase in VAT associated with 0.326 increase in SBP,Manual measurement of SBP, GRS;UKB
## 109 ,Adult,Systolic blood pressure,,Main,Per 1 SD ,SBP baseline in MDC Cohort, GRS ;UKBiobank
## 110 Manual measurement of SBP at baseline,Adult,Systolic blood pressure,weight divided by height in square metres,Main,Per 1 SD increase in GRS565 BMI,Manual measurement of SBP at baseline in MPP cohort, GRS score . UKB and GIANT for GRS weighting
## 111 ,Adult,Diastolic blood pressure,waist and hip circumference ratio,Main,Per 1 SD,DBP measured in MDC cohort at baseline. GRS;UKB and GIANT
## 112 ,Adult,Systolic blood pressure,Bioelectric impedance analysers,Main,Per 1 SD ,manual measurement of SBP in MDC cohort at baseline,GRS; UKB, Body fat GRS approximated on BMI
## 113 ,Adult,Systolic blood pressure,Bioelectrical impedance analyser,Main,Per 1 SD,Manual measurement of SBP, GRS, UKB
## 114 ,Adult,Systolic blood pressure,weight divided by height in square metres,Main,Per 1 SD increase,Manual measurement of SBP in MDC cohort, GRS;UKB and GIANT
## 115 ,Adult,Systolic blood pressure,weight divided by height in square metres,sensitivity,Per 1 SD,SBP measured in MPP cohort at followup, GRS;UKB and GIANT
## 116 ,Adult,Systolic blood pressure,waist and hip circumference ratio,sensitivity,Per 1 SD,SBP measured in MPP at followup, GRS;UKB and GIANT
## 117 ,Adult,Systolic blood pressure,weight divided by height in square metres,sensitivity,Per 1 SD increase in BF percentage,SBP measured in MPP at followup, GRS;UKB and GIANT
## 118 ,Adult,Systolic blood pressure,weight divided by height in square metres,sensitivity,Per 1 SD,SBP measured in MPP at followup, GRS;UKB and GIANT, VAT GRS on BMI
## 119 ,Adult,Diastolic blood pressure,weight divided by height in square metres,Main,Per 1 SD ,Diastolic blood pressure in MPP cohort, baseline measures, GRS;UKB and GIANT
## 120 ,Adult,Diastolic blood pressure,waist and hip circumference ratio,Main,Per 1 SD,DBP in MPP cohort baseline measurement, GRS;UKB and GIANT.
## 121 ,Adult,Diastolic blood pressure,bioelectric impedance analyser ,Main,Per 1 SD,DBP in MPP cohort at baseline, Body fat GRS on BMI, GRS;UKB
## 122 ,Adult,Diastolic blood pressure,MRI scan for VAT in UKBiobank for original GWAS ,Main,Per 1 SD ,DBP measured in MPP cohort at baseline, GRS;UKB,VATgrs on BMI
## 123 ,Adult,Diastolic blood pressure,MRI scan in UKB for VAT,sensitivity,Per 1 SD,DBP measured in MPP at followup, GRS;UKB, VAT GRS on BMI
## 124 ,Adult,Diastolic blood pressure,weight divided by height in square metres,Main,Per 1 SD ,DBP measured in MDC cohort, GRS;UKB
## 125 ,Adult,Diastolic blood pressure,Bioelectric impedance,Main,Per 1 SD,DBP measured in MDC cohort at baseline, GRS;UKB. BF GRS on BMI
## 126 ,Adult,Diastolic blood pressure,Bioelectric impedance,sensitivity,Per 1 SD,DBP measured in MPP at followup, GRS; UKB and GIANT, BF GRS on BMI
## 127 ,Adult,Diastolic blood pressure,MR scan of VAT in UKB,Main,Per 1 SD,DBP measured in MDC at baseline, GRS;UKB, VAT GRS on BMI
## 128 ,Adult,Diastolic blood pressure,waist and hip circumference ratio,sensitivity,Per 1 SD,DBP measured in MPP at followup, GRS;UKB and GIANT
## 129 ,Adult,Diastolic blood pressure,weight divided by height in square metres,sensitivity,Per 1 SD,DBP measured in MPP cohort follow up, GRS;UKB and GIANT
## 130 ,Childhood,Hypertension,weight divided by height in square metres,Main,odds of each change in weight category,UKBiobank cohort exposure variable, SNP-outcome from FinnGen study. Transformed the BMI into categorical data in UKBB.
## 131 ,Adult,Hypertension,Bioimpedance measurements,secondary,,Favourable adiposity (FA)
## 132 ,Adult,Hypertension,weight divided by height in square metres,Main,,Finngen cohort, BMI as exposure
## 133 ,Adult,Hypertension,Bioimpedance,secondary,,Unfavourable adiposity
## 134 ,Adult,Hypertension,Bioimpedance measures of body fat percentage,secondary,,Body fat percentage measured in UKBiobank
## 135 An automated reading form an Moron blood pressure monitor,Adult,Diastolic blood pressure,weight divided by height in square metres. Height measure to the nearest centimetre using a Seca 202 device and weight to the nearest 0.1 kg using Tanita BC418MA body composition analyzer.,Main,,The instruments were summarised into a weighted polygenic risk score similar to what is Lyalls paper. The weights derived form the effect estimated reported by GIANT (beta per 1-SD unit of BMI)
## 136 ,Childhood,Systolic blood pressure,Bioelectric impedance(fat mass percentage),Main,Per 1 SD increase in fat mass percentage(8.53),BCAMS, fat mass percentage, Genetic risk score
## 137 ,Childhood,Systolic blood pressure,waist circumference divided by height(WHtR),Main, per 1 SD increase in WHtR(0.07),BCAMS
## 138 ,Childhood,Systolic blood pressure,weight divided y height in square metres,Main,1 SD increase in BMI (4.93 kg/m2),Beijing Children and adolescents Metabolic Syndrome study
## 139 ,Childhood,Diastolic blood pressure,Bioimpedance -fat mass percentage,Main,Per 1 SD increase in fat mass percentage (FMP)8.53,BCAMS,GRS
## 140 ,Childhood,Diastolic blood pressure,waist circumference divided by height,Main,Per 1 SD increase in WHtR(0.07),BCAMS, GRS
## 141 ,Childhood,Diastolic blood pressure,weight divided by height in square metres,Main,1 SD increase in BMI(4.83kg/m2),BCAMS, GRS
## 142 Transthoracic echocardiography,Adult,Grade 1 Diastolic Dysfunction,weight divided by height in square metres,Main,1 SD increase in BMI,Vanderbilts biobank, no significant saps used SNPs at 10e-6
## 143 ,Adult,Gestational hypertension,weight divided by height in square metres,sensitivity,,Hypertension disorders during pregnancy. Two cohorts Giant(exposure) and FinnGen(outcome).
## 144 ,Adult,Gestational hypertension,weight divided by height in square metres,sensitivity,,Hypertension disorders during pregnancy. Two cohorts GIANT(exposure) and FinnGen(outcome)
## 145 Finger study description,Adult,Gestational hypertension,weight divided by height in square metres,Main,,hypertension disorders during pregnancy, two cohorts GIANT (exposure) and FinnGen (outcome).
## 146 Digital blood pressure monitors,Adult,Systolic blood pressure,weight divided by height in square metres,Main,,Z-transformation to standardise BMI, SBP and weighted PRS. Construct a weighted PRS using the variants from Giant consortium
## 147 Digital blood pressure monitors,Adult,Systolic blood pressure,weight divided by height in square metres,sensitivity,,z-transformation to standardise BMI, SBP and weighted PRS. Construct a weighted PRS using the variants from Giant consortium. MR-GENIUS
## ID
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## 2 Nicholas J Timpson et al_19470880_2009
## 3 Tove Fall et al_23824655_2013
## 4 Tove Fall et al_23824655_2013
## 5 Tove Fall et al_23824655_2013
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## 11 Tove Fall et al_25712996_2015
## 12 Tove Fall et al_25712996_2015
## 13 Tove Fall et al_25712996_2015
## 14 Tove Fall et al_25712996_2015
## 15 Tove Fall et al_25712996_2015
## 16 Tove Fall et al_25712996_2015
## 17 Tove Fall et al_25712996_2015
## 18 Tove Fall et al_25712996_2015
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## 22 Donald M loyal et al_28678979_2017
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## 28 Wes Spiller et al_30462199_2018
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## 32 Susanna C. Larsson et al_31195408_2020
## 33 Susanna C. Larsson et al_31195408_2020
## 34 Susanna C. Larsson et al_31195408_2020
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## 37 Susanna C. Larsson et al_31195408_2020
## 38 Torgny Karlsson et al_31501611_2019
## 39 Torgny Karlsson et al_31501611_2019
## 40 Torgny Karlsson et al_31501611_2019
## 41 Torgny Karlsson et al_31501611_2019
## 42 Frank Windmeijer et al_31708716_2018
## 43 Qiying Song_32636122_2020
## 44 Qiying Song_32636122_2020
## 45 Qiying Song_32636122_2020
## 46 Qiying Song_32636122_2020
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## 48 Ben Brompton et al_32665587_2020
## 49 Ben Brompton et al_32665587_2020
## 50 Ben Brompton et al_32665587_2020
## 51 Ben Brompton et al_32665587_2020
## 52 Ben Brompton et al_32665587_2020
## 53 Ben Brompton et al_32665587_2020
## 54 Ben Brompton et al_32665587_2020
## 55 Ben Brompton et al_32665587_2020
## 56 Ben Brompton et al_32665587_2020
## 57 Ben Brompton et al_32665587_2020
## 58 Ben Brompton et al_32665587_2020
## 59 Timothy E. Thayer_32712226_2021
## 60 Van Oort Sabine et al_33131310_2020
## 61 Van Oort Sabine et al_33131310_2020
## 62 Van Oort Sabine et al_33131310_2020
## 63 Van Oort Sabine et al_33131310_2020
## 64 Van Oort Sabine et al_33131310_2020
## 65 Van Oort Sabine et al_33131310_2020
## 66 Van Oort Sabine et al_33131310_2020
## 67 Van Oort Sabine et al_33131310_2020
## 68 Van Oort Sabine et al_33131310_2020
## 69 Van Oort Sabine et al_33131310_2020
## 70 Elina Hypponen_33323262_2019
## 71 Elina Hypponen_33323262_2019
## 72 Elina Hypponen_33323262_2019
## 73 Elina Hypponen_33323262_2019
## 74 Elina Hypponen_33323262_2019
## 75 Shan-Shan Dong et al_33771188_2021
## 76 Shan-Shan Dong et al_33771188_2021
## 77 Shan-Shan Dong et al_33771188_2021
## 78 Shan-Shan Dong et al_33771188_2021
## 79 Shan-Shan Dong et al_33771188_2021
## 80 Shan-Shan Dong et al_33771188_2021
## 81 Shan-Shan Dong et al_33771188_2021
## 82 Shan-Shan Dong et al_33771188_2021
## 83 Shan-Shan Dong et al_33771188_2021
## 84 Shan-Shan Dong et al_33771188_2021
## 85 Shan-Shan Dong et al_33771188_2021
## 86 Shan-Shan Dong et al_33771188_2021
## 87 Shan-Shan Dong et al_33771188_2021
## 88 Shan-Shan Dong et al_33771188_2021
## 89 Shan-Shan Dong et al_33771188_2021
## 90 Shan-Shan Dong et al_33771188_2021
## 91 Susan Martin et al_33980691_2021
## 92 Susan Martin et al_33980691_2021
## 93 Susan Martin et al_33980691_2021
## 94 Susan Martin et al_33980691_2021
## 95 Susan Martin et al_33980691_2021
## 96 Susan Martin et al_33980691_2021
## 97 Susan Martin et al_33980691_2021
## 98 Susan Martin et al_33980691_2021
## 99 Susan Martin et al_33980691_2021
## 100 Susan Martin et al_33980691_2021
## 101 Susan Martin et al_33980691_2021
## 102 Susan Martin et al_33980691_2021
## 103 Jingwen Fan_34001814_2021
## 104 Jingwen Fan_34001814_2021
## 105 Jingwen Fan_34001814_2021
## 106 Jingwen Fan_34001814_2021
## 107 Alice Giontella_34120448_2021
## 108 Alice Giontella_34120448_2021
## 109 Alice Giontella_34120448_2021
## 110 Alice Giontella_34120448_2021
## 111 Alice Giontella_34120448_2021
## 112 Alice Giontella_34120448_2021
## 113 Alice Giontella_34120448_2021
## 114 Alice Giontella_34120448_2021
## 115 Alice Giontella_34120448_2021
## 116 Alice Giontella_34120448_2021
## 117 Alice Giontella_34120448_2021
## 118 Alice Giontella_34120448_2021
## 119 Alice Giontella_34120448_2021
## 120 Alice Giontella_34120448_2021
## 121 Alice Giontella_34120448_2021
## 122 Alice Giontella_34120448_2021
## 123 Alice Giontella_34120448_2021
## 124 Alice Giontella_34120448_2021
## 125 Alice Giontella_34120448_2021
## 126 Alice Giontella_34120448_2021
## 127 Alice Giontella_34120448_2021
## 128 Alice Giontella_34120448_2021
## 129 Alice Giontella_34120448_2021
## 130 Grace M. Power_34465205_2021
## 131 Susan Martin et al_35074047_2022
## 132 Susan Martin et al_35074047_2022
## 133 Susan Martin et al_35074047_2022
## 134 Susan Martin et al_35074047_2022
## 135 Carlos Cinelli et al_35232963_2022
## 136 Liwan Fu et al_35599089_2022
## 137 Liwan Fu et al_35599089_2022
## 138 Liwan Fu et al_35599089_2022
## 139 Liwan Fu et al_35599089_2022
## 140 Liwan Fu et al_35599089_2022
## 141 Liwan Fu et al_35599089_2022
## 142 Nataraja Sarma Vaitinadin et al_35656995_2022
## 143 Wenting Wang et al_35694671_2022
## 144 Wenting Wang et al_35694671_2022
## 145 Wenting Wang et al_35694671_2022
## 146 Wes Spiller et al_35947639_2022
## 147 Wes Spiller et al_35947639_2022
